Beyond the water cooler: how online groups foster social capital for women in the skilled trades
Bibliographic record
Abstract
Purpose This paper examines the current gap between the labor supply and demand of skilled tradespeople. While it is often touted that women can be a potential source of skilled tradespeople to help bridge this gap, several barriers remain. The authors examined the role of social capital in the form of online support groups to attract and retain women in the skilled trades professions. Design/methodology/approach Qualitative interviews were conducted and recorded with 16 women in male-dominated skilled trades (e.g. electrician and plumber) in Canada. Thematic analysis was used to identify social capital, including the role of online support groups. Findings Unlike other studies that identified family and friends as crucial to developing social and cultural capital among women in the skilled trades, our participants noted that their friends and family were often not as supportive as they would have liked. Online support groups were mentioned as key to building social capital that helped these women to overcome various challenges such as ostracism, harassment and stigma. Originality/value There is a lack of research identifying the mechanisms involved in how women succeed in male-dominated trades. This research contributes to closing this gap.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".