Politically Related Boycotts Diminish Organizational Liking and May Have Unintended Consequences
Bibliographic record
Abstract
Polarization and rage fill the current political and social environment. Boycotts of organizations due to political stances are one important manifestation. However, there are no articles that empirically address the “micro” effects of boycotts. We examine how the existence of a boycott (based on politically related factors) of a simulated organization can influence job seeking. We report a main effect such that job seekers generally found boycotted organizations to be less similar to themselves and less liked by job seekers. We also found that job seekers who were politically dissimilar to a boycotted organization’s stance perceived themselves to be the least similar to that organization and liked it least. We also demonstrated that boycotts could have unintended consequences. While boycotters of an organization intend to coerce the organization into changing its political stance, the unintended effect of a boycott is on job seekers. The boycott discourages the very type of job seekers the external (boycotting) constituents would like to apply to change the organization and its political stance from within. Overall, boycotts can influence a variety of psychological processes such as job seeker perceived similarity, liking, realistic and symbolic threats, and intent to seek employment with an organization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".