The Role of Public Policy in the Retention and Advancement of Women in the Skilled Trades
Bibliographic record
Abstract
Women have been continually underrepresented in the skilled trades workforce in Alberta. In 2019, 4,600 of the 44,000 registered apprentices were women, roughly equating to 10% (Alberta 2020a). This report seeks to understand why the representation of women in this industry has been low and stagnant. Factors such as attraction, retention and advancement are explored as it relates to barriers women may be encountering that do not enable them to continue their pursuit of a career in the skilled trade industry. The exploration into the history of the skilled trades industry in Alberta exposes key issues and barriers experienced by women. A historical analysis of the Alberta skilled trades industry indicates that women have been underrepresented in the majority of certified trades. According to the annual Apprenticeship and Industry Training Statistical Profiles, issued by the Minister of Advanced Education, the number of female apprentice registrations is declining. The Canadians skilled trade industry is also at risk of a short term skills trade gap due to the decline in new apprenticeship registrations since 2014 (Canadian Apprenticeship Forum 2020). The annual Apprenticeship and Industry Training Statistical Profiles report also outlines “Traditional Trades” as trades that are biased toward women. Issues with reporting perpetuates stereotypes and does not allow for a deep understanding of the pathways women in the trades take. This definition is the only discussion of gender bias within the trades industry. The language used by the Alberta government reinforces stereotypes about women in the trades and does not allow for a deep understanding of the role gender bias plays in the exclusion of women from the majority of the certified trades in Alberta.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".