The Pervasiveness and Power of Political Affiliation/Ideology in Organizations
Bibliographic record
Abstract
The political landscape in the U.S. has become increasingly negative as members of major parties dislike each other more than at any time in the recent past. Some researchers characterize the dynamics as the “age of rage” (Webster, 2020). Against this backdrop we bring together four papers that outline the pervasive implications of political affiliation/ideology at work. Two papers suggest that political discourse at work, even if one is not engaging in it, has negative effects such as job dissatisfaction, rumination (on negative events), and insomnia as well as engaging in counterproductive work behaviors. A third paper addresses the explanatory variables that help understand why political affiliation/ideology related variables influence hiring. Results suggest that suspicion and trustworthiness are key variables such that suspicion helps predict negative outcomes such as expected CWBs while trustworthiness helps explain positive outcomes such as expected task performance. Our last paper addresses the issue of how political affiliation influences reactions toward anti-racism programs and shows that political affiliation was the strongest antecedent of reactions toward such programs and toward organizational attraction. All told, the papers suggest political affiliation/ideology is a critical variable with pervasive effects on employees’ cognitions, emotions, and well-being, as well as consequences for organizations. Political Discourse at Work: How and Why Ambient Political Conversations Impact Employee Outcomes Presenter: Maira Elize Ezerins; U. of Arkansas, Fayetteville Presenter: Young Eun Lee; Florida State U. Presenter: Christopher C. Rosen; U. of Arkansas Presenter: Philip L. Roth; Clemson U. Enough Already! The Implications of Political Discourse at Work Presenter: Cecily Cooper; U. of Miami Presenter: Kurt T Dirks; Washington U. in St. Louis Presenter: Craig D. Crossley; U. of Central Florida Presenter: Sharon Sheridan; College of Business, Clemson U. The Influence of Political Affiliation in the Workplace: The Role of Trustworthiness and Suspicion Presenter: Philip L. Roth; Clemson U. Presenter: Phil Bobko; Gettysburg College Presenter: Jason Thatcher; Fox School of Business, Temple U. Presenter: Wenxi Pu; U. of Manitoba Partisanship vs. Principles for Policies:What Drives Responses to Anti-Racist Workplace Initiatives Presenter: David A. Harrison; U. of Texas at Austin Presenter: Alison V. Hall; U. of Texas At Arlington Presenter: Laura Garcia; -
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".