Bibliographic record
Abstract
Welcome to a very important book.Dr. Lynn Gehl describes the effort she made to document the sex discrimination affecting her and her family, her decision to challenge that discrimination, and how she applied herself to various proceedings from 1994 until she achieved a victory in the Ontario Court of Appeal in 2017.She offers crucial insights into the struggle of women for equality under the Indian Act, the latest stage of which has occupied more than sixty years.With knowledge and experience from years of advocacy before Parliament as well as the courts, and the depth of perception typical of all her scholarly work, Lynn assesses what more is needed before the Indian Act system can be truly egalitarian.This book is unique and inspiring.The book is unique because it is the only full-length, first-person account of a leading case about discrimination against women in the Indian Act of Canada.Gehl v Canada is the fifth in a series of iconic court challenges to Canada's long-standing attempt to assimilate Indigenous Peoples by expelling Status Indian women from their communities.In the 1960s, the battle against discrimination and assimilation was fought largely outside of the courtroom, with activists such as Mary Two-Axe Earley of Kahnawake and Jenny Margetts of Alberta, along with Indigenous women's organizations, raising awareness and seeking amendments to the
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.564 | 0.444 |
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".