The influence of industrial structure on female labour force participation in Canadian urban areas
Bibliographic record
Abstract
An understanding of the factors influencing participation is important to planners both for manpower planning and related purposes and because of the implications for social and economic well-being. Although the participation rates of women, particularly married women, have risen dramatically in recent decades, wide regional differences remain^ Most studies of participation in Canada have focussed either on individual characteristics or on the response to unemployment conditions. This study argues that, because women's employment is highly concentrated in a few industries and occupations, the industrial composition of local labour markets is likely to be an important factor, inhibiting participation where few jobs are available. An attempt is made to measure this influence by including in a multiple regression analysis of 1971 Census data an index variable representing, for 101 Census Metropolitan Areas and Census Agglomerations, the extent to which industrial structure favours women's employment. This variable is expected to show a significant positive association with female participation rates; its inclusion is expected to increase the explanatory power of the model and to reduce the influence of the dummy variables reflecting 'independent' regional factors. The analysis, however, provides only limited support for these hypotheses. A consistent positive association is revealed, but, for most age and marital status groups, this is not statistically significant. Regional influences appear to be reflecting industrial structure factors only slightly and, in the case of Quebec, not at all. Factors which may account for this disappointing result are discussed, in particular, shortcomings in the proxies themselves and the prevalence of strong relationships among the independent variables. The hypothesised relationship appears to be one which is not readily reflected in a study of this type; some suggestions are therefore made for further research. Consideration is nevertheless given to alternative policy measures applicable to areas where industrial structure does appear to inhibit participation, the conclusion being that, unless accompanied by vigorous application of equal opportunity and "equal pay for work of equal value" measures, the encouragement of "female-intensive" industries would provide only a partial solution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".