Workfare: why good social policy ideas go bad
Bibliographic record
Abstract
One of the greatest, as well as the most debated, social policy ideas of the 1980s and 1990s was workfare. In Workfare: Why Good Social Policy Ideas Go Bad, Maeve Quaid delves into the definition and history of workfare, and then continues with a critical and comparative analysis of workfare programs in six jurisdictions: three American (California, Wisconsin, New York) and three Canadian (Alberta, Ontario, New Brunswick). Drawing from these case studies, Quaid develops an analytic model that illustrates how workfare falls prey to a series of hazards whereby good social policy ideas fail. Their demise, argues Quaid, begins with politicians with a zest for big ideas but little interest in implementation, continues with short-sighted policy makers, resistant bureaucrats, cynical recipients, flawed evaluations, and is completed by fleeting and fickle public attention for these news stories. Quaid's identification and analysis of these hazards is especially valuable because the hazards can also be applied to innovation in any area of social policy, such as health-care, education, pension plans, child-care, and unemployment insurance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".