Replicating and extending the political affiliation model of hireability ratings: Suspicion, an enhanced outcome space, and causal chain analyses
Bibliographic record
Abstract
Hostility toward members of opposing political parties is at record levels. To address this hostility and polarization, we test theory outlined in the political affiliation model (PAM), including constructive replication and extensions with the variables of identification, disidentification, perceived similarity, and liking. We also replicate the role of suspicion as it fits in PAM, and examine the effect of party versus candidate effects on expected counterproductive workplace behaviors (CWBs), expected influence on coworker attitudes, and expected turnover in. Finally, we further test and strengthen our findings by incorporating experimental manipulations of suspicion and liking (via causal chain analysis). Results of three studies provide support for most of the presumed key relationships in PAM. In general, liking is a key mediator to positive behaviors such as expected task, OCB, and coworker attitudes while suspicion is a key mediator for negative expected behaviors such as CWBs and expected turnover. Overall, PAM receives substantial support via replications and extensions to new variables that include expected CWBs, turnover, and recommendations to interview to help understand how political forces influence judgments in the workplace.
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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 | 0.094 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".