Welfare to Work: The Next Generation A National Forum
Bibliographic record
Abstract
This paper presents final results from the Self-Sufficiency Project (SSP) study of welfare applicants.1 SSP was a Canadian research and demonstration project designed to test a policy innovation intended to make work pay better than welfare. Conceived and funded by Human Resources Development Canada (HRDC), managed by the Social Research and Demonstration Corporation (SRDC), and evaluated by the Manpower Demonstration Research Corporation (MDRC) and SRDC, SSP offered a temporary financial incentive to single parent, long-term income assistance (IA) recipients who left income assistance for full-time work. The SSP Applicant study recruited single parents who were newly receiving income assistance (referred to in this paper as “Applicants”). These Applicants were told that if they stayed on income assistance for a full year they would become eligible for an earnings supplement. They would start to receive the SSP supplement if they then left income assistance for full-time work within the subsequent 12 months (i.e. their second year on income assistance). SSP offered a supplement paid on top of earnings from employment for up to three years, provided recipients worked 30 or more hours each week and remained off
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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.003 |
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".