Bibliographic record
Abstract
This article provides information on women aged 25 to 64 in natural and applied sciences occupations in Canada (i.e. scientific occupations), using data from the 1991 and 2001 censuses and the 2011 National Household Survey (NHS). The employment conditions of men and women in these occupations are also examined, based on data from the Labour Force Survey (LFS). Key findings include: from 1991 to 2011, the proportion of women in scientific occupations requiring a university education rose from 18 per cent to 23 per cent, and from 14 per cent to 21 per cent in scientific occupations requiring a college education; during the same period, the proportion of women in non-scientific occupations requiring a university education increased from 59 per cent to 65 per cent, and from 41 per cent to 44 per cent in non-scientific occupations requiring a college education; between 1991 and 2011, women accounted for 27 per cent of the growth in the number of workers in university-level scientific occupations, but for 75 per cent of the growth in the number of workers in university-level non-scientific occupations; computer science accounted for 60 per cent of the increase in the number of workers in scientific occupations requiring a university education - the smaller contribution of women to the overall increase in the number of scientific workers is related to the fact that they accounted for a smaller share of workers in computer science occupations; workers in scientific occupations generally have better employment conditions - on average, men working full-time earned nine per cent more than their female counterparts in both scientific and non-scientific occupations.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".