Bibliographic record
Abstract
While the existence of occupational gender segregation is well known, it has been usual to see it as a reflection of women’s disadvantage. However, cross-national data show that the greater the segregation, the less tends to be women’s disadvantage. The solution to this puzzle entails the introduction of the two orthogonal dimensions of segregation, where only the vertical dimension measures inequality while the horizontal dimension measures difference without inequality. Furthermore, the two dimensions tend to be inversely related, with a tendency for the horizontal component to be larger and so have more effect on the resultant overall segregation; hence the inverse relation between overall segregation and inequality. The usual explanations of segregation, being focused on inequality, are inadequate. To understand the situation it is necessary to take account of the many related factors in social change, and to recognize that horizontal segregation reduces opportunities for gender discrimination within occupations. An exploratory test of the argument is conducted for the US, Canada and Britain. With pay as the vertical dimension the results are essentially as predicted. With CAMSIS, a measure of occupational advantage, a slight advantage lies with women. The test is less clear but consistent with the argument.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".