Replicating and extending the political affiliation model of hireability ratings: Suspicion, an enhanced outcome space, and causal chain analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| 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 | 0.000 |
| 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 it