Unpaid work and social policy: Engaging research with mothers on social assistance
Bibliographic record
Abstract
The interlocking issues of gender, unpaid work and multiple forms of representation or lived experiences with social policy are complex. The study ‘Who Benefits: Women, Unpaid Work and Social Policy’, supported by Status of Women Canada, and guided by an advisory group consisting of women’s and anti-poverty organizations was based in Saskatchewan, Canada. The study interrogated how mothers on social assistance (SA) defined and understood unpaid caregiving work with small children; and the impact of social welfare policy guidelines that pushed SA recipients to find paid employment. Using action research and original, creative methods to gather data, the research simultaneously created a non-threatening environment for discussion, information-sharing, support and knowledge creation among participants. Overall, findings in the study resonate with other published studies on low-income women and unpaid work. Unique to this study particularly, were the action research process and outcomes which provided ways to address the needs of the study participants and to catalyze participant-led actions. The study assisted the 28 participants in linking their unpaid work with social policy and finally, in taking socio-political action. Actions included meetings with government, press conferences, and an uptake of recommendations by advisory group organizations. Independent of the research, the participants continued to meet after the study concluded.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".