Walking on Eggshells: Abused Women's Experiences of Ontario's Welfare System
Bibliographic record
Abstract
Research project highlighting the adequacy of the Ontario's welfare system for abused women. Key Findings: 1. Benefit levels are wholly inadequate to meet real costs of rent, food, accommodation, transportation and other living expenses; 2. Women are staying in or returning to abusive relationships because of inadequate welfare rates; 3. Women are not supported in their desire and efforts to become employed; 4. Women are required to pursue child support in situations that put their safety at risk; 5. Abusive partners use the threat of welfare fraud charges to control and intimidate women; 6. Critical information about benefits, rules and entitlements are not disclosed to women; 7. The vague, complex definitions of 'spouse' and 'same-sex partner' make women wary of forming new relationships; 8. Women find their experiences on welfare to be similar to their experiences of abuse; Key Recommendations: 1. Raise rates to meet true costs; 2. Stop the national child benefit supplement clawback; 3. Provide meaningful training and supports for employment, including assistance for education; 4. Redesign support obligation policies; 5. Revamp fraud policies and practices; 6. Provide accurate, complete information; 7. Change worker attitudes towards recipients.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".