Making occupations possible? Critical narrative analysis of social assistance in Ontario, Canada
Bibliographic record
Abstract
Social assistance is a program created to alleviate extreme poverty by providing payments to people with little or no income. It has been heavily criticized due to the conflicting nature of its two main objectives, alleviating poverty and promoting self-sufficiency. The purposes of this research were to present a richly textured account of the lived experience of persons receiving social assistance in Ontario, Canada and to explore how their occupational possibilities are influenced by broader social contexts and policy. We used critical narrative analysis, which combines hermeneutic phenomenology with critical theory, to interrogate the data from a governmentality perspective. We uncovered common aspects of participant experiences related to the social system, the community, and individual factors and demonstrated tensions created by neoliberalism: the Neoliberal Paradox, the Welfare-to-Work Paradox, and the Caseworker Paradox. Social assistance recipients lack the opportunity and resources to make everyday choices and to have decision-making power as they participate in occupations. Through a better understanding of the social and political processes that create social assistance, while considering the lived experience of its recipients, occupational scientists will be better able to identify and rectify occupational injustices for people living in poverty.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.037 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".