Women's Transitions from Welfare: "Where Does That Leave Us? Employable or in the Pit?"
Bibliographic record
Abstract
In a globalized market training, education, and credentials are important as individuals compete for spaces in the workplace. For some, like single mothers on welfare, acquisition of training and credentials becomes a long, arduous, circuitous "walk" as they attempt to enter the labour force. This article examines how government policies and labour market demands influence women's transitions from welfare, explores the experiences of women in a case study, and analyzes the role that using brainstorming, popular education techniques, and content in a pre-employment upgrading program had on women's educational and employment needs as they attempted to leave welfare. Résumé Dans un marché de formation désormais mondial, l'éducation et les références d'emploi sont essentielles lorsqu'on est en compétition pour une place dans le marché du travail. Pour certains, comme les mères célibataires vivant de l'aide sociale, acquérir de la formation et avoir des références devient un processus long, ardu, voire circulaire, lorsqu'ils cherchent à s'intégrer au marché du travail. À travers une étude de cas, cet article examine comment les politiques gouvernementales et les exigences du marché de l'emploi influencent l'issue de la démarche desfemmes qui tentent de sortir de l'aide sociale. Il explore des expériences de femmes et analyse le rôle de l'utilisation du remue-méninge et des techniques et des contenus d'éducation populaire dans un programme de pré-emploi visant à combler les besoins éducatifs de ces femmes à mesure qu'elles tentent de quitter l'aide sociale.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".