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Algorithmic amplification of politics on Twitter –




The role of social media in political discourse has been the topic of intense scholarly and public debate. Politicians and commentators from all sides allege that Twitter’s algorithms amplify their opponents’ voices, or silence theirs. Policy makers and researchers have thus called for increased transparency on how algorithms influence exposure to political content on the platform. Based on a massive-scale experiment involving millions of Twitter users, a fine-grained analysis of political parties in seven countries, and 6.2 million news articles shared in the United States, this study carries out the most comprehensive audit of an algorithmic recommender system and its effects on political content. Results unveil that the political right enjoys higher amplification compared to the political left.


Content on Twitter’s home timeline is selected and ordered by personalization algorithms. By consistently ranking certain content higher, these algorithms may amplify some messages while reducing the visibility of others. There’s been intense public and scholarly debate about the possibility that some political groups benefit more from algorithmic amplification than others. We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2 million daily active accounts to a reverse-chronological content feed free of algorithmic personalization. We present two sets of findings. First, we studied tweets by elected legislators from major political parties in seven countries. Our results reveal a remarkably consistent trend: In six out of seven countries studied, the mainstream political right enjoys higher algorithmic amplification than the mainstream political left. Consistent with this overall trend, our second set of findings studying the US media landscape revealed that algorithmic amplification favors right-leaning news sources. We further looked at whether algorithms amplify far-left and far-right political groups more than moderate ones; contrary to prevailing public belief, we did not find evidence to support this hypothesis. We hope our findings will contribute to an evidence-based debate on the role personalization algorithms play in shaping political content consumption.

Political content is a major part of the public conversation on Twitter. Politicians, political organizations, and news outlets engage large audiences on Twitter. At the same time, Twitter employs algorithms that learn from data to sort content on the platform. This interplay of algorithmic content curation and political discourse has been the subject of intense scholarly debate and public scrutiny (115). When first established as a service, Twitter used to present individuals with content from accounts they followed, arranged in a reverse chronological feed. In 2016, Twitter introduced machine learning algorithms to render tweets on this feed called Home timeline based on a personalized relevance model (16). Individuals would now see older tweets deemed relevant to them, as well as some tweets from accounts they did not directly follow.

Personalized ranking prioritizes some tweets over others on the basis of content features, social connectivity, and user activity. There is evidence that different political groups use Twitter differently to achieve political goals (1720). What has remained a matter of debate, however, is whether or not any ranking advantage falls along established political contours, such as the left or right (2, 7), the center or the extremes (1, 3), specific parties (2, 7), or news sources of a certain political inclination (21). In this work, we provide systematic quantitative insights into this question based on a massive-scale randomized experiment on the Twitter platform.

Experimental Setup

Below, we outline this experimental setup and its inherent limitations. We then introduce a measure of algorithmic amplification in order to quantify the degree to which different political groups benefit from algorithmic personalization.

When Twitter introduced machine learning to personalize the Home timeline in 2016, it excluded a randomly chosen control group of 1% of all global Twitter users from the new personalized Home timeline. Individuals in this control group have never experienced personalized ranked timelines. Instead, their Home timeline continues to display tweets and retweets from accounts they follow in reverse chronological order. The treatment group corresponds to a sample of 4% of all other accounts who experience the personalized Home timeline. However, even individuals in the treatment group do have the option to opt-out of personalization (SI Appendix, section 1.A).

The experimental setup has some inherent limitations. A first limitation stems from interaction effects between individuals in the analysis (22). In social networks, the control group can never be isolated from indirect effects of personalization, as individuals in the control group encounter content shared by users in the treatment group. Therefore, although a randomized controlled experiment, our experiment does not satisfy the well-known Stable Unit Treatment Value Assumption from causal inference (23). As a consequence, it cannot provide unbiased estimates of causal quantities of interest, such as the average treatment effect. In this study, we chose to not employ intricate causal inference machinery that is often used to approximate causal quantities (24), as this would not guarantee unbiased estimates in the complex setting of Twitter’s home timeline algorithm. Building an elaborate causal diagram of this complex system is well beyond the scope of our observational study. Instead, we present findings based on simple comparison of measurements between the treatment and control groups. Intuitively, we expect peer effects to decrease observable differences between the control and treatment groups; thus, our reported statistics likely underestimate the true causal effects of personalization.

A second limitation pertains to the fact that differences between treatment and control groups were previously used by Twitter to improve the personalized ranking experience. The treatment, that is, the ranking experience, has therefore not remained the same over time. Moreover, the changes to the treatment depend on the experiment itself.

Measuring Amplification

We define the reach of a set T of tweets in a set U of Twitter users as the total number of users from U who encountered a tweet from the set T.* Think of T, for example, as tweets from a group of politicians in Germany, and think of the audience U as all German Twitter users in the control group. We always consider reach within a specific time window, for example, a day.

We define the amplification ratio of set T of tweets in an audience U as the ratio of the reach of T in U intersected with the treatment group and the reach of T in U intersected with the control group. We normalize the ratio in such a way that amplification ratio 0% corresponds to equal proportional reach in treatment and control. In other words, a random user from U in the treatment group is just as likely to see a tweet in T as is a random user from U in the control group. An amplification ratio of 50% means that the treatment group is 50% more likely to encounter one of the tweets. Large amplification ratios indicate that the ranking model assigns higher relevance scores to the set of tweets, which therefore appear more often than they would in a reverse chronological ordering.

We often study the amplification ratio in cases where T is a set corresponding to tweets from a single Twitter account (individual amplification). When considering how groups of accounts are amplified, we have the choice between reporting distribution of amplification ratios of the individual accounts in the group or considering a single aggregate amplification ratio (group amplification), where T contains all tweets authored by any member of the group. We generally report both statistics. More detail on how we calculate amplification and a discussion of the difference between individual and group amplification is found in SI Appendix, section 1.D.


We divide our findings into two parts. First, we study tweets by elected politicians from major political parties in seven countries which were highly represented on the platform. In the second analysis, which is specific to the United States, we study whether algorithmic amplification of content from major media outlets is associated with political leaning.

We first report how personalization algorithms amplify content from elected officials from various political parties and parliamentary groups. We identified Twitter account details and party affiliation for currently serving legislators in seven countries from public data (2528) (SI Appendix, section 1.B). The countries in our analysis were chosen on the basis of data availability: These countries have a large enough active Twitter user base for our analysis, and it was possible to obtain details of legislators from high-quality public sources. In cases where a legislator has multiple accounts—for example, an official and a personal account—we included all of them in the analysis. In total, we identified 3,634 accounts belonging to legislators across the seven countries (the combined size of legislatures is 3,724 representatives). We then selected original tweets authored by the legislators, including any replies and quote tweets (where they retweet a tweet while also adding original commentary). We excluded retweets without comment, as attribution is ambiguous when multiple legislators retweet the same content. When calculating amplification relating to legislators, we considered their reach only within their respective country.

To compare the amplification of political groups, we can either calculate the amplification of all tweets from the group (group amplification; Fig. 1 A and B) or calculate amplification of each individual in the group separately (individual amplification; Fig. 1C). The latter yields a distribution of individual amplification values for each group, thus revealing individual differences of amplifying effects within a group.

Fig. 1A compares the group amplification of major political parties in the countries we studied. Values over 0% indicate that all parties enjoy an amplification effect by algorithmic personalization, in some cases exceeding 200%, indicating that the party’s tweets are exposed to an audience 3 times the size of the audience they reach on chronological timelines. To test the hypothesis that left-wing or right-wing politicians are amplified differently, we identified the largest mainstream left or center-left and mainstream right or center-right party in each legislature, and present pairwise comparisons between these in Fig. 1B. With the exception of Germany, we find a statistically significant difference favoring the political right wing. This effect is strongest in Canada (Liberals 43% vs. Conservatives 167%) and the United Kingdom (Labor 112% vs. Conservatives 176%). In both countries, the prime ministers and members of the government are also members of the Parliament and are thus included in our analysis. We, therefore, recomputed the amplification statistics after excluding top government officials. Our findings, shown in SI Appendix, Fig. S2, remained qualitatively similar.

When studying amplification at the level of individual politicians (Fig. 1C), we find that amplification varies substantially within each political party: While tweets from some individual politicians are amplified up to 400%, for others, amplification is below 0%, meaning they reach fewer users on ranked timelines than they do on chronological ones. We repeated the comparison between major left-wing and right-wing parties, comparing the distribution of individual amplification values between parties. When studied at the individual level, a permutation test detected no statistically significant association between an individual’s party affiliation and their amplification.

We see that comparing political parties on the basis of aggregate amplification of the entire party (Fig. 1 A and B) or on the basis of individual amplification of their members (Fig. 1C) leads to seemingly different conclusions: While individual amplification is not associated with party membership, the aggregate group amplification may be different for each party. These findings are not contradictory, considering that different politicians may reach overlapping audiences. Even if the amplification of individual politicians is uncorrelated with their political affiliation, when we consider increases to their combined reach, group-level correlations might emerge. For a more detailed discussion, please refer to SI Appendix, section 1.E.3.

Our fine-grained data also allow us to evaluate whether recommender systems amplify extreme ideologies, far-left or far-right politicians, over more-moderate ones (3). We found that, in countries where far-left or far-right parties have substantial representation among elected officials (e.g., VOX in Spain, Die Linke [The Left] and AfD [Alternative for Germany] in Germany, and La France Insoumise and Reasemblement national [National Rally] in France), the amplification of these parties is generally lower than that of moderate/centrist parties in the same country (SI Appendix, Fig. S1). Finally, we considered whether personalization consistently amplifies messages from the governing coalition or the opposition, and found no consistent pattern across countries. For example, in the United Kingdom, amplification favors the governing Conservatives, while, in Canada, the opposition Conservative Party of Canada is more highly amplified.

Tweets from legislators cover just a small portion of political content on the platform. To better understand the effects of personalization on political discourse, we extend our analysis to a broader domain of news content (30, 31). Specifically, we extend our analysis to media outlets with a significant audience in the United States (32). While the political affiliation of a legislator is publicly verifiable, there is no single agreed-upon classification of the political orientation of media outlets.

To reduce subjectivity in our classification of political content, we leverage two independently curated media bias–rating datasets from AllSides (33) and Ad Fontes Media (34), and present results for both. Both datasets assign labels to media sources based on their perceived position on the US media bias landscape. The labels describe the overall media bias of a news source on a five-point scale ranging from partisan Left through Center/Neutral to partisan Right. We then identified tweets containing links to articles from these news sources shared by anyone between 1 April 2020 and 15 August 2020. We excluded tweets pointing to nonpolitical content such as recipes or sports. Wherever possible, we separated editorial content from general news coverage, as, in some cases, these had different bias ratings (SI Appendix, section 1.C). The resulting dataset contains AllSides annotations for 100,575,284 unique tweets pointing to 6,258,032 articles and Ad Fontes annotations for 88,818,544 unique tweets pointing to 5,100,381 articles.

We then grouped tweets by media bias annotation of their source and calculated the aggregate amplification of each bias category (Fig. 2). When using AllSides bias ratings (Fig. 2A), two general trends emerge: The personalization algorithms amplify sources that are more partisan compared to ones rated as Center. Secondly, the partisan Right is amplified marginally more compared to the partisan Left. The results based on Ad Fontes bias ratings (Fig. 2B) differ in some key ways. Most notable is the relatively low, 10.5%, amplification of the partisan Left compared to other categories. Among the remaining categories, the differences are not substantial, although the Neutral category is amplified significantly less than other categories.

<a href="" title="Amplification of news articles by Twitter’s personalization algorithms broken down by AllSides (A) and Ad Fontes (B) media bias ratings of their source. Blue squares denote the mean estimate of group amplification for each group of content, and error bars show the SD of the bootstrap estimate. Individual black circles show the amplification for the most significant positive and negative outliers within each group. For example, content from AllSides “Left” media bias category is amplified 12% by algorithms. The most significant negative outlier in this group is BuzzFeed, with an amplification of –2% compared to the chronological baseline. By contrast, Vox is amplified 16%. Negative and positive outliers are selected by a leave-one-out procedure detailed in SI Appendix, section 1.E.4." class="highwire-fragment fragment-images colorbox-load" rel="gallery-fragment-images-1799645959" data-figure-caption="

Amplification of news articles by Twitter’s personalization algorithms broken down by AllSides (A) and Ad Fontes (B) media bias ratings of their source. Blue squares denote the mean estimate of group amplification for each group of content, and error bars show the SD of the bootstrap estimate. Individual black circles show the amplification for the most significant positive and negative outliers within each group. For example, content from AllSides “Left” media bias category is amplified 12% by algorithms. The most significant negative outlier in this group is BuzzFeed, with an amplification of –2% compared to the chronological baseline. By contrast, Vox is amplified 16%. Negative and positive outliers are selected by a leave-one-out procedure detailed in SI Appendix, section 1.E.4.

” data-icon-position data-hide-link-title=”0″>Fig. 2.Fig. 2.

Fig. 2.

Amplification of news articles by Twitter’s personalization algorithms broken down by AllSides (A) and Ad Fontes (B) media bias ratings of their source. Blue squares denote the mean estimate of group amplification for each group of content, and error bars show the SD of the bootstrap estimate. Individual black circles show the amplification for the most significant positive and negative outliers within each group. For example, content from AllSides “Left” media bias category is amplified 12% by algorithms. The most significant negative outlier in this group is BuzzFeed, with an amplification of –2% compared to the chronological baseline. By contrast, Vox is amplified 16%. Negative and positive outliers are selected by a leave-one-out procedure detailed in SI Appendix, section 1.E.4.

Leave-one-out analysis of each media bias category (described in detail in SI Appendix, section 1.E.4) allows us to identify the most significant outliers in each category, also shown in Fig. 2. This analysis identified BuzzFeed News, LA Times, and Breitbart (based on both AllSides and Ad Fontes ratings) as negative outliers in their respective categories, meaning the amplification of their content was less than the aggregate amplification of the bias category they belong to. Meanwhile, Fox News and New York Post were identified as positive outliers. These outliers also illustrate that, just as we saw in the case of legislators, there is significant variation among news outlets in each bias category.

The fact that our findings differ depending on the media bias dataset used underlines the critical reliance of this type of analysis on political labels. We do not endorse either AllSides or Ad Fontes as objectively better ratings, and leave it to the reader to interpret the findings according to their own assessment. To aid this interpretation, we looked at how AllSides and Ad Fontes ratings differ, where both ratings are available. We found that, while the two rating schemes largely agree on rating the political right, they differ most in their assessment of publications on the political left, with a tendency for Ad Fontes to rate publications as being more neutral compared to their corresponding AllSides rating. Details are shown in SI Appendix, Figs. S3 and S4 and Table S1.


We presented a comprehensive audit of algorithmic amplification of political content by the recommender system in Twitter’s home timeline. Across the seven countries we studied, we found that mainstream right-wing parties benefit at least as much, and often substantially more, from algorithmic personalization than their left-wing counterparts. In agreement with this, we found that content from US media outlets with a strong right-leaning bias are amplified marginally more than content from left-leaning sources. However, when making comparisons based on the amplification of individual politician’s accounts, rather than parties in aggregate, we found no association between amplification and party membership.

Our analysis of far-left and far-right parties in various countries does not support the hypothesis that algorithmic personalization amplifies extreme ideologies more than mainstream political voices. However, some findings point at the possibility that strong partisan bias in news reporting is associated with higher amplification. We note that strong partisan bias here means a consistent tendency to report news in a way favoring one party or another, and does not imply the promotion of extreme political ideology.

Recent arguments that different political parties pursue different strategies on Twitter (14, 15) may provide an explanation as to why these disparities exist. However, understanding the precise causal mechanism that drives amplification invites further study that we hope our work initiates.

Although it is the largest systematic study contrasting ranked timelines with chronological ones on Twitter, our work fits into a broader context of research on the effects of content personalization on political content (2, 3, 9, 21) and polarization (3538). There are several avenues for future work. Apart from the Home timeline, Twitter users are exposed to several other forms of algorithmic content curation on the platform that merit study through similar experiments. Political amplification is only one concern with online recommendations. A similar methodology may provide insights into domains such as misinformation (39, 40), manipulation (41, 42), hate speech, and abusive content.

Materials and Methods

The Timelines Quality Holdback Experiment.

Twitter has maintained the randomized experiment described in Experimental Setup since June 2016. Accounts were randomly assigned to treatment or control either at the experiment’s onset or at the time the account was created. As of 5 June 2020, the experiment included 58 million unique Twitter user IDs (58,087,969, 5% of all accounts globally), of which 20% (11,617,373) are assigned to control, and 80% (46,470,596) are assigned to the treatment group. About 12% of studied accounts (∼ 7 million) logged in within a single day of the study, and about 20% (∼ 12 million) logged in within a single week. More information about the tweet selection, presentation, and ranking in either group, as well as the services and machine learning models influencing the content that users are exposed to through their Home Timeline, is provided in SI Appendix, section 1.A.

Ethical and Data Protection Reviews.

The control group assessed was not created for the purpose of research but rather for the business purpose of improving the algorithm and providing a baseline to which it could be compared to monitor the ongoing performance of the algorithm. As such, this work was reviewed by Twitter’s legal and privacy teams as part of its ordinary business operations (and not an IRB). As part of this review, a data protection impact assessment was conducted, and it was determined that additional notice and consent mechanisms were not required.

Obtaining Legislators’ Twitter Details.

We identified countries to include in our analysis based on the following criteria: 1) availability of data on politicians’ Twitter accounts and 2) sufficient Twitter user base in the country. Screening for these criteria, we identified the following list of countries: United States, Japan, United Kingdom, France, Spain, Canada, Germany, and Turkey. Turkey was then excluded, due to limited availability of legislators’ accounts for the current, 27th term (only about 18% of current legislators had a valid Twitter account). To identify members of the current legislative term in each country, we relied on Wikidata, public Twitter lists, and official government websites. While, in most countries, we were able to identify Twitter details of over 70% of all representatives following automated methods, our goal was to ensure that potentially missing accounts would not result in poor representation of certain minority groups in our dataset. We, therefore, focused manual annotation efforts on ensuring that accounts of legislators who belong to certain underrepresented groups are included in our dataset. In most countries, we were able to retrieve gender labels from Wikidata to aid with this process.

To test various hypotheses about the types of political parties algorithms might amplify more, we make some direct comparisons between parties in each country. We rely on the 2019 Chapel Hill Expert Survey (29) and Wikidata annotations to determine the ideological position of each party. More information on the data collection process from the aforementioned resources and groupings of parties is provided in SI Appendix, section 1.B.

Media Bias Ratings.

We obtained media bias ratings for news sources from AllSides (33) and Ad Fontes Media (34). While the former includes news sources with a global audience, it focuses primarily on the US media landscape, and the media bias ratings relate to how the media bias of these sources is perceived in the United States. We excluded sites like Yahoo News and Google News, as well as podcasts, content studios, and activist groups whose original content was not clearly identifiable or attributable. To qualify for our analysis, the content from the publication source had to be clearly identifiable on the basis of URLs shared by users on the platform. AllSides provides categorical labels of media bias, while Ad Fontes provides numerical media bias ratings that are discretized into different categories based on the media bias chart.

To identify URLs that link to articles from each publication, we created regular expressions, which were matched against the text of the URL. We then identified tweets with content from these publications by screening public tweets created between 1 March and 30 June 2020, and matching any URLs these tweets contained against the regular expressions we curated. The resulting dataset contained AllSides annotations for 100,575,284 unique tweets pointing to 6,258,032 different articles and Ad Fontes annotations for 88,818,544 unique tweets pointing to 5,100,381 different articles. More information on the media bias ratings and the regular expressions used can be found in SI Appendix, section 1.C.

Measuring Amplification.

Our measures of amplification are based on counting events called “linger impression,” that is, events registered every time at least 50% of the area of a tweet is visible for at least 500 ms. Linger impressions are the best proxy available to us to tell whether a user has been exposed to the content of a tweet.

Let T denote a set of tweets. Let Ucontrol and Utreatment denote the control and treatment groups of users, respectively, in the experiment. Note that, in our experiment, |Utreatment|=4|Ucontrol|. Let Ut,d denote the set of users who registered a linger impression with tweet t on day d. For a set of tweets T, we further define UT,d=∪t∈TUt,d, the set of users who encountered at least one tweet from T on day d. We define the amplification of the set of tweets T on day d asad(T)=(|UT,d∩Utreatment|+14|UT,d∩Ucontrol|+1−1)·100%.[1]

Often, we consider amplification within a specific country. In this case, we calculate the above ratio based on impression events from an IP address that we identified to be within country c.

When we talk about the amplification of a group G of individuals, such as members of a political party, we mean the amplification of the set of all tweets created by this group TG. The amplification for a group of users G is therefore a(G)=a(TG). Analogously, when referring to the amplification of an individual user i, we calculate this based on the set of tweets, Ti, or, equivalently, the group amplification for the singular set containing only i, that is, a(i)=a(i). A more detailed explanation of the group and individual amplification, as well as their differences, is presented in SI Appendix, sections 1.D and 1.E.

Data Availability

Aggregated study data are available upon request from the corresponding authors following the protocol outlined in SI Appendix, section 3.


We thank Ayşe Naz Erkan, Wenzhe Shi, Parag Agrawal, Ariadna Font Llitjós, Rumman Chowdhury, Nick Pickles, and Julian Moore for feedback and support of this work.


    • Accepted October 5, 2021.
  • Author contributions: F.H., S.I.K., A.S., and M.H. designed research; F.H., S.I.K., C.O., L.B., and M.H. performed research; F.H., S.I.K., C.O., and A.S. analyzed data; F.H., S.I.K., and M.H. wrote the paper; C.O. curated datasets; and L.B. coordinated the internal review process.

  • Competing interest statement: F.H., S.I.K., C.O., L.B., and A.S. are or were employed by Twitter while this work was carried out. F.H. was a full-time employee of Twitter when the work was started, but left Twitter in July 2020 and continued being affiliated with Twitter through a paid part-time consulting relationship. M.H. was a paid consultant for Twitter. C.O., L.B., and A.S. have a financial interest in Twitter.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at

  • *A tweet is counted as “encountered” by user A when 50% of the UI element containing the tweet is continuously visible on the user’s device for 500 ms. See SI Appendix, section 1 for details.

  • Ad Fontes Media Bias Chart 5.0.


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Canada, echoing U.S., says it fears armed conflict could erupt in Ukraine



Canada fears armed conflict could break out in Ukraine and is working with allies to make clear to Russia that any more aggression towards Kiev is unacceptable, Prime Minister Justin Trudeau said on Wednesday.

U.S. Secretary of State Antony Blinken said earlier that Russia could launch a new attack on Ukraine at “very short notice”. Moscow, which has stationed military equipment and tens of thousands of troops near the border, denies it is planning an invasion and blames the West for rising tensions.

“We do fear an armed conflict in Ukraine. We’re very worried about the position of the Russian government … and the fact that they’re sending soldiers to the Ukrainian border,” Trudeau told a news conference.

Canada, with a sizeable and politically influential population of Ukrainian descent, has taken a strong line with Russia since its annexation of Crimea from Ukraine in 2014.

“We’re working with our international partners and colleagues to make it very, very clear that Russian aggression and further incursion into Ukraine is absolutely unacceptable,” Trudeau said.

“We are standing there with diplomatic responses, with sanctions, with a full press on the international stage.”

Canadian troops are in Latvia as part of a NATO mission and Trudeau said they would “continue the important work that NATO is doing to protect its eastern front”.

Canada has had a 200-strong training mission in western Ukraine since 2015.

Canadian Foreign Minister Melanie Joly on Tuesday said Ottawa would make a decision at the appropriate time on supplying military hardware to Ukraine.

Trudeau side-stepped a question about sending defensive weapons, saying any decision would “be based on what is best for the people of Ukraine”.

(Reporting by David Ljunggren;Editing by Will Dunham and Philippa Fletcher)

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Opinion: Canadians will pay the price for the Liberals playing politics with trucking – Calgary Herald



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With Canadians grappling with inflation not seen in a generation, the federal government has decided to throw fuel on the fire.


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On Saturday, the Liberals’ vaccine mandate for international truckers came into effect, an ill-conceived move that will drive up the price of goods imported from the United States and exacerbate driver shortages and, more so, our national capacity to export Canadian goods.

Even without the mandate, today we have nearly 23,000 openings for professional drivers and counting — a vacancy rate already at a record high.

When we think of front-line workers, nurses, doctors and grocery store clerks are usually the first who come to mind. There is another occupation, however, that needs to be added to that list: truck drivers. Throughout the pandemic, tens of thousands of hard-working Canadians have been working round the clock to keep our supply chains moving.


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Let me be clear: the Canadian trucking industry is strongly supportive of efforts to increase vaccine uptake among Canadians. Safe and effective, the vaccine is far and away the best way to prevent serious illness or death from COVID-19. The Alberta Motor Transport Association, for instance, partnered with the governments of Alberta and Montana to offer vaccine clinics for cross-border truckers.

Thanks to efforts such as these, the majority of truckers are fully vaccinated. Indeed, the vaccination rates among many Canadian Trucking Alliance members are well above the national average. As we have since the vaccine became available, we will continue to encourage our members to roll up their sleeves. This doesn’t change the incremental impacts of putting our MVPs — our professional drivers — on the bench.


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That’s now our reality, thousands of truckers will be sidelined by this policy change. According to our data, the exit rate for the 120,000 truck drivers currently crossing the border will be between 10 and 15 per cent. Late last Wednesday evening, Canadians thought we had a reprieve on this direction, only to be rescinded within 24 hours. This flip-flop leadership just reinforced the confusion within the federal government on this issue.

And that, unfortunately, is just the beginning. The government has signalled that there will be amendments imminently under the Canada Labour Code, mandating any truck or bus drivers who cross a provincial border (federally regulated employees) to require vaccination. While the regulatory language, enforcement measures and penalties are still unclear, this government policy will force a driver whose route runs from Medicine Hat to Swift Current, Lethbridge to Cranbrook, or one side of a border town like Lloydminster to the other to choose between vaccination and working in our industry.


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The federal government has snubbed meaningful engagement on this mandate. A consultation paper was posted on Dec. 7 and days later the process was closed to comments. Although Ottawa claimed to have engaged with stakeholders, the government clearly still doesn’t understand the severity of the outcome from a policy decision limiting Canadians ability to support bilateral trade or interprovincial mobility. At every step of the way, our industry has pleaded with the government to work with us on solutions, including regularly testing to keep our drivers behind the wheel, to no avail.

By putting politics ahead of common sense, the federal government is throwing up more roadblocks for a critical industry that is already under tremendous stress. As a result, Canada’s already fragile supply chains are going to be stretched even further.


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What does that mean for Canadians? Well, get ready for more bare shelves and to open your wallets even wider for what is left. From food, to gas, to consumer goods, things are going to get even more expensive; that is if they make it to the shelf.

The cost of bringing a truckload of fruit and vegetables from California has already doubled during the pandemic due to the existing driver shortage. As Canadian fields lie fallow and covered in snow, produce prices will only go higher.

As is always the case with bad policies and bad politics, it’s going to be Canadians who are left holding the bag.

Jude Groves is the board chair of the Alberta Motor Transport Association.



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How Women Can Get Comfortable “Playing Politics” at Work – Harvard Business Review



By now, it’s a tired refrain: Women, particularly women of color, are significantly outnumbered at the senior leadership level in organizations. Covid-19 made this fact worse: In 2021, the number of years it would take before women reached parity with men increased by a third. The pandemic essentially erased all the gains made by women of the last decade, and it may take several decades to recover to pre-Covid levels.

The causes of the leadership gender gap are numerous, as are its proposed solutions. One area of research points to differences concerning women’s response to “office politics.” Politics, broadly defined as being able to successfully navigate the unwritten rules of “how things get done and through whom,” includes understanding the motivations of others at work and using this knowledge to influence in ways that enhance one’s personal interest and organizational objectives.

In our experience as psychologists and coaches, we have found that many women have an adverse, almost allergic reaction, to office politics. Numerous studies confirm this; women tend to see it as something “dirty” or dishonest, and as a stressful aspect of work that reduces their job satisfaction.

And yet, by nature, humans are relational beings and political skill matters. It is a necessary part of organizational life. Studies affirm that being able to successfully use political skills is critical to career advancement.

We recognize that engaging in office politics can be stressful. It often forces people to stretch beyond their natural preferences and patterns. We aim to offer ways to participate in politics that reduces discomfort and maximizes career advancement.

This article identifies some commonly held beliefs underlying women’s aversion to being political at work. Next, it offers mindset shifts that have helped hundreds of women use political skills to their advantage.

5 Reasons Women Dislike Office Politics

1. My work should speak for itself.

Being political contradicts many people’s belief in meritocracy. The notion that one has to do more than excel at work itself is seen as anathema to men and women alike. However, for women and other marginalized groups who have to work twice as hard to counter the bias related to their gender and race, this can be experienced as an even greater insult and burden.

2. Building connections is an extracurricular activity.

Cultivating political relationships often feels extraneous and distracting from the work, like just another item on a to-do list. And for women, who spend, on average, 37% more time than men on housework and chores in addition to their full-time jobs, the idea that they have to find more space and time for these additional activities feels unreasonable.

3. It’s inauthentic.

Politics is often seen as posturing, making alliances with those who have clout or supporting initiatives that are popular simply for the sake of staying close to the power source. To many, this can feel inauthentic and, at times, duplicitous.

4. I don’t like playing hardball.

Office politics often plays out as a “zero-sum game,” involving gossip, backstabbing, sabotaging, and even intimidation. Women, and a fair number of men, have an aversion to these tactics, and prefer power that is based on influence, relationships, and win-win approaches.

5. The penalties are too great.

Women are penalized for displaying political skill. Studies show that women are judged more harshly for being assertive or competitive, two common characteristics of office politics. And, consequently, they are penalized for it.

Do you hold any of these beliefs? If so, it’s understandable. There’s validity to them. And yet, if you don’t challenge them, you may be limiting your potential. In our work, we have found that cultivating the following five mindsets is an effective way to help counter these beliefs and embrace and develop political skills.

5 Ways to Shift Your Mindset Around “Playing Politics”

1. From “My work should speak for itself” to “It’s my responsibility to show people how my work connects to theirs.”

No one is an island. When people, male or female, believe their work should speak for itself, they fail to recognize the interdependence of organizational life. Believing your work should speak for itself is a narrow, functional view of a job, one that assumes others can fully appreciate and comprehend the part you play in the larger organizational puzzle.

We typically see this belief in two groups. The first is from very technical leaders — those with a highly valued, specialized area of expertise. It’s easy for these individuals to see how the organization depends on what they provide, but it’s less obvious to them how their work depends on others.

We also have heard this response from those who are more comfortable with a hierarchical style of leadership and who have a more deferential relationship to management. They question the necessity of advocating for themselves, seeing it as the task of their manager to see and evaluate their performance.

When we work with leaders people on making a shift away from this mindset, we focus on transitioning from a functional or expert mindset to an enterprise one, one that enables people to connect their area of expertise to the larger business needs. In other words, to think in terms of what’s best for the whole organization, not just their small part of it.

One of us coached a senior executive who rose rapidly through the ranks from director to vice president in a very technical, male-dominated field. She navigated the politics in her rise to the top by learning how to connect her work to others’ work. Before every conversation, every meeting, and every presentation, she would take five minutes to anticipate the possible blow-back or resistance she could incur. She took a careful inventory of her audience, considering who they were, what their needs were, and the priorities they were facing. She would then consider ways to connect her contributions to their needs, positioning herself as a necessary and intrinsic part of everyone else’s success. By carefully tying her work to others and to the organization’s goals, she tied her success to the success of others, thereby ensuring that they saw the value in what she had to offer.

2. From “Building connections is an extracurricular activity” to “Building connections is a force multiplier.”

Work gets done with and through people. And the higher up you go, the more this is true. In the interdependent world of work, where you need others to help you accomplish your goals, continuously nurturing relationships and learning from others is key to your success.

For example, attending a women’s conference can double a woman’s likelihood of receiving a promotion within a year, triple the likelihood of a 10%+ pay increase within a year, and increase her sense of optimism by up to 78%, immediately. Something powerful happens when people engage with others. People are more inspired. They learn new strategies for career advancement. They are exposed to new ideas. They build confidence in asking for what they need and maybe even find a way to share their wisdom with others.

When we work with leaders on making a shift away from this mindset, we help them see the benefits, not just the burden, of making connections. We host six-month leadership development programs within organizations where participants have the opportunity to meet, repeatedly, as cohorts. Women who are seeking new opportunities, stuck in their career trajectory, or those struggling with leadership tensions find it productive to hear from others in similar positions, to learn new approaches for promoting themselves, and to see alternatives for managing their challenges.

In the final session, participants give a five-minute presentation on a topic that has big career implications after rehearsing and revising their presentations in small groups. These dress-rehearsals give people the opportunity to hone their stories, more clearly articulate their facts, and bolster their stage presence for maximum effectiveness. Countless participants credit the feedback from their new network with helping them adjust and sharpen their presentations to the point that they ultimately land funding, drive new strategy and galvanize followers. In several instances, the women also helped each other find new roles, transition into different departments, and gain access into new and influential networks. In other words, the relationships built in the program and the perspectives gathered from those relationships help our participants amplify their impact.

3. From “It’s inauthentic” to “I’m being paid to have a point of view and share it.”

The research on authenticity shows that it requires two things: conscious awareness (knowing who you are, your motives, and what you’re bringing to the current situation) and expression (consciously aligning your behavior with your awareness). It means acting in accordance with your true feelings, thoughts, and highest intentions in a way that serves the context. Authenticity requires discernment, courage, and self-determination. It’s not a reaction to what’s happening around you; it’s relating to the players and situation from a grounded sense of who you are.

You’re more negatively affected by office politics if you don’t know what you stand for or don’t have the courage to advocate for it. To be political — and authentic — you must know what your values and intentions are so that you can move projects and teams forward in a way that aligns with you and the organization’s goals. In some ways, it’s easier for people to be against politics than it is to get clear on what they stand for and champion it.

When we work with people on making a shift away from this mindset, we help them discern their purpose and values so they can make choices in alignment with them.

One of us coached a woman who was discouraged by the leadership behavior of the senior leaders in her business unit. As a result, rather than seeking promotion to the next level, she was considering quitting. Through coaching, she realized that her decision was a reaction to her colleagues’ behavior; yet, she hadn’t defined the leadership behavior she valued. By helping her clarify her own leadership point of view, she felt inspired to model new behaviors and open up conversations inside her business unit about the role leaders play in creating the culture. This changed her attitude towards her current job, and she felt more inspired and motivated to stay in the role, and even apply for a promotion. Rather than reacting to what she disliked, she made a conscious decision to be a role model for the leadership behavior she wanted to see present in her organization.

4. From “I’m not someone who plays hardball” to “My leadership tactic needs to match the situation.”

Political behavior can be a turn-off, especially when it involves hard power tactics: coercion, intimidation, and sabotage. For many people, men and women alike, this is what “being political” means, as opposed to using softer power tactics of persuasion, building alliances, and offering assistance.

Yet, power, hard or soft, is neither good nor bad. What makes the use of power good or bad is the motivation behind its use and the impact it has on others. While it’s easy to see the negative applications of hard power, soft power can also be misused, or used to villainous ends. Consider how Bernie Madoff, Jeffrey Skilling, and Jim Jones employed persuasion, charisma, and relationship-building.

When we work with leaders on making a shift away from this mindset, we help them understand that their application of hard or soft power tactics should be situational, not a matter of preference or style. Some situations call for hard power and some for soft power. Specifically, hard power tactics may be needed to hold people accountable, make tough and unpopular decisions, set boundaries, or enact consequences to inappropriate workplace behavior.

One of us coached a leader who had a decided preference for soft power tactics. She worked in a creative industry in which her collaborative style worked well at first. But within a few months of her leading a new team, team members began to complain about burnout. Shortly thereafter, a few senior team members quit due to conflict. This led her to look at the dynamics on the team and how her leadership was a factor.

Through discussions with each team member, she realized that her collaborative approach had resulted in team meetings being dominated and derailed by a few vocal members. Agendas were often hijacked by tangential discussions and meetings often ended without clarity and direction, forcing people to spend hours in discussion to recap and rehash the outcomes.

Our client learned to incorporate hard power tactics to match the team dynamic. She began to intervene, set boundaries, create rules for conversation, and hold people accountable if they failed to follow the meeting guidelines. It was a revelation to her to realize that collaborative leadership had its limits, and that harder power tactics can also have a place.

5. From “The penalties are too great” to “I prioritize my growth.”

Women are penalized for being ambitious and displaying political skill. The research is clear: Negative stereotypes have negative consequences for one’s career. It’s true that women and minorities pay a steep price for displaying ambition.

And yet, for many, the alternative may be worse. While the blowback to displaying ambition is tough, so too is the personal and psychological toll of not striving to fulfill your potential and not stretching to reach your goals. For many women and minorities, waiting for the world to change before they can assert themselves is a steeper price to pay than the backlash of being ambitious.

The mindset here is one of prioritizing growth. But this shouldn’t be done naively. It’s important to be prepared and to consider the consequences you may face. You may need to gather resources and allies, and ensure you have the support in your personal and professional life before undertaking any action. And above all, it’s important to have a Plan B, or even a Plan C in place. Consider, realistically, the penalties you may face. Do you have alternatives in mind if things don’t work out as planned? Are you prepared to switch business units or even companies if necessary?

A growth mindset (the belief that talents can be developed through hard work, good strategies, and input from others) is protective against negative stereotypes. For example, one study found that when Black university students were taught to have a growth mindset, they were less likely to internalize the negative stereotype directed at them, and thus, had better outcomes in their studies. On the other hand, students with a fixed mindset, seeing themselves as unable to change, were more prone to suffer the effects of the negative stereotyping.

One of us coached a woman who described her manager as someone who stifled her ambition, denied her access to senior leaders, and routinely took credit for her work. She felt pushed out by her manager with no option but to leave the firm. Through coaching she realized that she had, in fact, mastered her role. There wasn’t room to learn new skills, create more impact or meet new stakeholders. Her lack of opportunity had as much to do with her role’s limited scope as it had to do with her disparaging manager.

By recognizing her need for growth, she decided to intentionally seek a new role with more scope and impact potential outside her firm. Rather than feeling “chased out,” she realized her old position was more limiting than her leader. This mindset shift made her the hero of the story instead of the victim.

The harsh reality is that women and racialized minorities face discrimination, negative stereotypes, and hostility. But there are choices to be made, choices which provide more flexibility and resilience, or less. Preparing yourself, gathering allies and resources, having a Plan B in place, and developing a growth mindset that frames the challenge as an opportunity to learn and grow, can be powerful protection for the backlash you may face.

. . .

Office politics impact your work experience and your projects, whether you participate in them or not. We advocate it’s better to be a player than a pawn. The women we coach want to be leading at the highest levels, and yet many have not examined their limiting beliefs about using political skills to advance their careers. The mindset you bring to any situation, especially one that can be experienced as negative and aversive, is critical to your success.

As a reader, did you notice yourself agreeing with any of the beliefs outlined above? If so, can you see a way to shift your mindset that gives you more power over your experience and possibilities in your career?

Office politics matters because as relational beings, getting ahead is as much about people and relationships as it is about skills and experience. Your ability to participate in politics, and to employ your political skills is not just critical to career advancement, but equally important for your well-being at work.

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