Special Edition: Fighting Feminism – Organised Opposition to Women’s Rights; Guest Editors’ Introduction
Bibliographic record
Abstract
This special issue presents a series of papers by scholars who participated in a workshop entitled ‘Men's Groups: Challenging Feminism’, which was held at the University of British Columbia (UBC), Canada, 26-27 May 2014. The workshop was organised by Susan B Boyd, Professor of Law and Chair in Feminist Legal Studies at the UBC Faculty of Law, and was sponsored by the Peter Wall Institute for Advanced Studies at UBC, the Peter A Allard School of Law, the Centre for Feminist Legal Studies at UBC, and the Canadian Journal of Women and the Law. The aim of the workshop was to bring together feminist scholars from multiple disciplines and multiple national contexts to explore a source of resistance to feminism that has been largely overlooked in scholarly research: the growing number of nationally situated and globally linked organisations acting in the name of men's rights and interests which contend that men are discriminated against in law, education and government funding, and that feminism is to blame for this. This special edition presents eight papers inspired by the workshop, authored by scholars from Canada, New Zealand, Poland, Sweden and the United States. A second special issue comprised of eight other papers inspired by the workshop was published in the Canadian Journal of Women and the Law as volume 28(1) in 2016.To find out more about this special edition, download the PDF file from this page.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
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".