Money for the marginalized: Promoting access to income benefits in Manitoba
Bibliographic record
Abstract
Low-income Manitobans are eligible for a variety of federal and provincial income support benefits that may help them meet their basic needs. However, many face barriers to completing the bureaucratic processes required to access these benefits. In response, nonprofit and public sector agencies have developed free benefit intermediary programs that support low-income community members to claim their benefits. Despite the growth of this field, there is a dearth of scholarly literature on programs that promote access to income benefits. This thesis contributes to filling this gap through a mixed-methods study of benefit intermediary programs operating in Winnipeg, MB. Using a realist evaluation methodology, this study examines contextual conditions that inhibit benefit take-up and the field of social programs that promote access to benefits. The evaluation considers the social-structural context, program goals and activities, and key program mechanisms that may account for the outcomes that ensue. Findings from semi-structured key informant interviews and a literature review demonstrate that benefit intermediary programs have dual objectives that correspond to two of Nancy Fraser’s strategies for achieving social justice. At the individual level, they employ a strategy of affirmative redistribution to assist low-income community members to claim benefits that increase their quality of life. At a structural level, they pursue nonreformist reforms to reduce systemic barriers that inhibit benefit take-up and build cross-sectoral capacity to promote access. However, these programs face constraints that limit the scope of their direct service delivery and the extent to which they can effect structural change. Nevertheless, benefit intermediaries play a vital role in promoting access to income benefits in Manitoba. This research may be useful for practitioners, policymakers, and social scientists who are interested in the problem of benefit non-take-up, or who are engaged in efforts to increase the take-up of money for the marginalized.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".