Stereotypes or competition? Analyzing certain gender preferences among employers
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
Stereotypes or competition? Analyzing certain gender preferences among (by Alexandra Moskovskaya) analyzes role of gender stereotypes in work process including evaluation of male and female labor by employers and employees. Data base consists of survey results of labor relations effected by the Center for labor market research under Russian-Canadian CIDA project Rising female 158 competitiveness in the labor market in Russia. Three kinds of data have been analyzed 1 Basic characteristics of men and women workers by the very workers and their employees. 2. Evaluation by employers of their own actions in hypotethical situations. 3. Data from real practices in production units The analysis permitted to find out contradictory positions of both employers and working men and women, as well as divergences between subjective assessments by respondents with the facts of life in their workshops. A basic conclusion is linked to hypotheses that competition between men and women leads to a considerable influence on employer's posture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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