Bibliographic record
Abstract
The hiring process is currently probably the least understood aspect of the employment relationship. It may very well be the most important for understanding the broad processes of stratification with allocation by sex and race to jobs and firms. A central reason for the lack of knowledge is that it is very difficult to assemble extensive data on the processes that occur at the point of hiring. We analyzed data on all applicants to a large service organization in the U.S. in a 16‐month period in 1993–1994. We investigated the rating at the time of application, the probability of getting hired, and the ratings achieved one, three, and six months after hire. Overall differences between men and women were (a) negligible in rating received at the time of application, (b) small but slightly in favor of women in probability of getting hired, and (c) clearly in favor of women for ratings after hire. The evidence points unambiguously in one direction: Women do not come out worse than men in the hiring process in this organization. To the extent there is a difference, it is to the advantage of women. However, if the posthire performance ratings are free of sex bias, then women should have been hired at an even higher rate. When analyses were done separately by occupation, there are few differences between men and women in getting hired in the three occupations accounting for 94 percent of hires. In the other two, only 8 and 15 hires were made, making statistical analysis less meaningful. However, there is evidence that blacks face a disadvantage in getting hired, and also receive lower ratings after hire. Hispanic men are especially disadvantaged in getting hired.
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.
How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".