A Theory of Time-Based Discrimination in Evaluation
Bibliographic record
Abstract
Career inequality across demographic groups remains pervasive in professional and managerial roles. Given the importance of long work hours to professional and managerial culture, we posit that biased evaluations of individuals’ time spent working may importantly shape these inequalities. Synthesizing scholarship on inequality with theory on subjective time, we theorize a process of time-based discrimination in evaluation. We suggest that demographic tokens—numerical minorities in a given context—may experience discriminatory evaluation of their work time. We posit that key aspects of the temporal context in professional and managerial jobs amplify evaluators’ emphasis on work time in evaluations. When assessing tokens, evaluators’ time-based stereotypes about the token group’s characteristics may be activated. Some tokens may be stereotyped as “shirkers,” experience scrutiny of their work hours, and get interpreted as being misfits with the dominant culture on the basis of low hours worked. Other tokens may be stereotyped as “overworkers,” experience scrutiny of their natural ability, and get interpreted as being misfits on the basis of low talent. Together, these processes may negatively affect performance assessments and evaluations of leadership potential for tokens. We delineate contributions and new lines of research for scholarship on career inequality and subjective time.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".