Making the Shift from Pink Collars to Blue Ones: Women's Non-Traditional Occupations
Bibliographic record
Abstract
As GOVERNMENT STATISTICS INDICATE, men are disproportionately represented in the trades and industrial occupations. Women are quite simply so few in number as to be non-existent or invisible; hence, for women such employment often re ferred to as non-traditional. Maria Charles and David Grusky ponder whether this gender imbalance is best regarded as organic feature of modern economics.1 Gillian Creese characterizes it as an important feature of contemporary labour markets.2 Two factors help explain the persistent absence of women from the trades and industrial occupations. The first that the work itself gendered3 or sex-typed.4 It viewed by most people, almost without second thought, as men's work. The trades and industrial occupations are, by their very nature, understood to be masculine because those who fill them have a gender and their gender rubs off on the jobs they mainly do.5 As Cynthia Cockburn observes, work designated male or female by ascribing a series of polarized characteristics, complementary paired values, to the 'masculine' and the 'feminine'. Normally men and women, things and jobs, comfortably reflect
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".