Bibliographic record
Abstract
The Culture of Construction: Or, Etiquette for the Nontraditional Kate BraidBoth of my aunts worked in wartime industry, one as an inspector in aircraft construction and the other making ammunition.But in 1977, when I got my Wrst job in construction in British Columbia as a labourer -apart from my aunts (and stories of other "Rosies" in the Second World War) -I had never heard of women in traditionally male-dominated jobs in construction.I had certainly never met one, and for good reason.I would later Wnd out that in 1977 women were less than 3 percent of the nontraditional workforce.1 In the ensuing twenty-Wve years, numerous women -including meachieved their skilled trades qualiWcations to become journey "women," 2 and support services have bloomed.We have seen studies, reports, conferences, and courses to introduce women to trades work.Provincial trades training schools have made speciWc commitments to train women, 3 organizations have been formed for and by women in trades, there are summer go-cart building camps for girls, occasional commitments to afWrmative action, and role models galore.And still the number of women in trades remains stuck at around 3 percent.4 The obvious question is "Why?"Speculation abounds.Tradeswomen's organizations, word of mouth, and some trades schools have afWrmed that the numbers of women entering skilled blue collar work -the recruitment aspect of trades -has increased.Yet with roughly the same number of women in trades today as existed twenty-Wve years ago, we have failed miserably at retention -at keeping them there.Why?After Wfteen years as a construction labourer, apprentice, journey carpenter, union member, trade school instructor, researcher, and owner/ operator of my own construction business, I have become convinced that the answer is not simply in the obvious -in pervasive sexual harassment by male members of the trade.The answer is more complex.One woman
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.036 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".