Bibliographic record
Abstract
les femmes rurales et les mkres monoparentales se retrouvent complktement exclues du marcbt! du travail. Leur seulefapn dj, revenir est le programme du (< Ontario Works' Workfare M. Malheureusement, cepro-gramme fait tr2s peu pour les aider ri. sortir de h pauvrett!. Toutefis avec h participation du gouvernement, il y a une solution. A woman faces many challenges in her lifetime, including parenting chil-dren or caring for elderly parents. These challenges may remove her from the workforce for a time, leav-ing her economically vulnerable. And, for many women, returning to the world of paid employment is very difficult, especially if they are from rural communities where jobs are scarce. Women living in rural areas and small towns are at particular risk of needing government assistance, as are single mothers. It isn't because they don't want to support their fami-lies; rather, they are unable to find employment. If they do find a job, often it does not provide enough to keep the family's bills paid and mouths fed. According to Statistics Canada's 2001 Census data 36.6 per cent of all low-income families in Ontario are headed by a woman, and those families have an average deficit of over $10,000 per year. More people living in Rural Small Town (RST) areas have been found to be "persistently poorer " than those who live in Large Urban Centres (LUC) (Phimister and Weersink). And, unfortunately, women are a large subset of the persistently poor. According to their analysis of 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.022 |
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".