<scp>J</scp>ack (and <scp>J</scp>ill?) of All Trades – A <scp>C</scp>anadian Case Study of Equity in Apprenticeship Supports
Bibliographic record
Abstract
Abstract In the past decade, Canadian federal and provincial governments have designed programmes to facilitate entry into trades in an attempt to stimulate economic growth. As part of these efforts, increasing attention is focusing on programmes to encourage women to enter skilled trades, while paying little attention to those trades traditionally dominated by females. In this article, we explore the gendered dimensions of apprenticeship programmes in Canada, demonstrating the ways in which gender inequality is reproduced by programmes that situate employers and women as responsible for change. In particular, using a case study, we illustrate that the gendered structure of the labour market is preserved and reproduced. While efforts have targeted women to facilitate entry into non‐traditional occupations such as electricians and plumbers, female‐dominated trades such as hairstylists remain untouched, thereby sustaining the gendered wage structure of the economy. Thus women remain segregated in low‐paying trades and receive fewer public supports when pursuing training in these segregated trades. The article argues that apprenticeship training and certification is constructed to respond to the needs of male‐dominated trades, but not the needs of female‐dominated trades. Ultimately, the public policy decisions that make up the apprenticeship training and certification system in Canada reproduce gender inequality.
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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 | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".