Bibliographic record
Abstract
In many countries, women are the fastest growing group of unionized workers. As unions scramble to restore their flagging membership, women become central to the process of union membership renewal. Yet survey data collected from union organizers in Canada show that unions are only partially meeting women’s demand for union representation, in large part because of gender bias in union organizing practices. To develop this argument, this article offers data analysis that challenges four popular misconceptions about women and unions which contribute to gender bias in union organizing practices. These misconceptions are: women are less likely to support unions than men; high rates of unionization in the public sector rather than women themselves explain the high rates of union growth amongst women; small workplaces are a particular barrier to organizing women and women are more passive and avoid conflict, therefore reducing their likelihood of withstanding a hostile organizing drive. Having challenged these misconceptions, the article concludes with a discussion of the many ways in which union organizing practices are gender biased. Issues discussed range from the limited number of women hired as organizers to the tendency of unions to target small male‐dominated workplaces for organizing, over women‐dominated workplaces, in spite of the latter’s greater likelihood of success.
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 | 0.090 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.093 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".