It's a man's world! The role of political ideology in the hiring process for leadership positions
Bibliographic record
Abstract
Previous research on gender discrimination has explored whether and why women are less likely than men to occupy leadership positions in organizations. The current research contributes to this literature by exploring one form of discrimination, subtle and rather unexpected in nature, as it occurs during the hiring process when the decision maker presents information about a leadership position to a potential job candidate. Drawing on role congruity theory and research on political ideology, we predict an interaction effect between a job candidate’s gender and the decision maker’s political ideology on the way the information about a position is presented. In the first two studies, we demonstrate that conservative (but not liberal) decision makers present more positive information about a leadership position to male, versus female, job candidates. In an effort to assess the gravity of this subtle form of discrimination, we conducted a third study, wherein we demonstrate that the position description conservative decision makers typically provide to female job candidates is deemed by participants as less attractive than the one they typically provide to male job candidates. Implications and future direction are discussed.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".