Bibliographic record
Abstract
ii This study seeks to understand how a client's voice is transmitted through an advocate who is representing them in front of a Social Benefits Tribunal (SBT). Three clients and three advocates were separately interviewed in a southwestern Ontario city for an average of fifty minutes. While not specifically trained to work in an adversarial system, the literature reflects that social workers can be well suited to work in settings such as the SBT. This study reports that clients felt that their advocate accurately represented their voice within the hearings and that their voice was stronger than it would have been without the advocate. The participants also shared that there are many ways the SBT, ODSP frontline staff and administrative procedures both hear and silence their voice. This study suggests that the application process for ODSP should be made more simplified and user friendly. It concludes that the weighting of the client application forms and treatment of medical evidence should be clarified. While advocates typically perform their jobs with a high level of excellence, it is felt there is some room to enhance
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.117 | 0.032 |
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".