Bibliographic record
Abstract
This paper exunines the isslrc of welfcne recidiuism m Toronto. Afeer reviewing the auailable literature - both in ter-ms of tndiuidual experiences ad. the broader bbow market context - the paper reports on the findings of a telephone suruey of social assistance Ieauers conducted inToronto in 2005. Analysis reveak irnpornnt differences in terms of age , family tJpe ad education between recidiuists cnd the general social asslstan ce case- Load. Typical "work-first" employment. interq.tentrons a'e shown to be krgely ineffecnue in preuenting retums to asslstance . Additional finarcial assistance , employment md medical bcncfits arc aII shown to haqte positivc effects. A rcmge of meanues described as "beyond work-first" (firuncial incenti,Des and supports, case m(magement, employer-based strategies, and greater access to education cmd training both before and after employment) offer some fn'omise of inueasing job retention and reducing recidiq,'ism. Howeuer, there is also a need to question the asswnPtion tfutt. retLLms to assistctnce reflect a policy failwe. While the goal must be on sustained ernployment, quicl
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.595 | 0.210 |
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".