Bibliographic record
Abstract
Discussion of welfare reform in Ontario is largely focused on highly visible issues: reduced benefits, tighter eligibility requirements and increased work requirements. However, a less well publicized, but equally important, set of changes has been designed and implemented. This concerns the way welfare is delivered or the “Service Delivery Model ” (SDM). A recent review of welfare reform initiatives across Canada concluded that administrative practices have as important an effect on outcomes as any other component of reform (Human Resources Development Canada, 2000). Burdensome and inflexible requirements create administrative pretexts for denying benefits or, as the authors of the review express it, simply “‘scare ’ people away from applying. ” This article argues that while the stated goals of the SDM are to reduce costs, enhance program integrity and improve client services, the real intent is to restrict entry and reduce benefits. This systematic denial occurs as social assistance applicants are discouraged, diverted and disentitled through cumbersome and complicated application and appeals processes, deliberately confusing procedures and language and excessive and inappropriate requests for information. Evidence of this is shown through reference to two significant changes: the introduction of a two-step application process and ongoing eligibility verification.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".