Bibliographic record
Abstract
Workfare: Why Good Social Policy Ideas Go Bad, Maeve Quaid, Toronto: University of Toronto Press, 2002, pp. 244 This book begins with the premise that workfare, properly administered, is good social policy. The author dismisses the moral arguments surrounding this policy: i) that workfare distinguishes the deserving from the undeserving; ii) that workfare is a form of slavery, forcing the poor to work in order to survive; iii) that workfare creates important responsibility for the recipient; iv) that workfare safeguards welfare recipients' status as citizens able to fully participate in a democratic society. These are controversial moral assertions about the merits or demerits of workfare that the author refuses to address. Instead, the author, as an expert in organizational behaviour and human resource management, is interested in whether this policy meets the goals it establishes. If workfare is to lead recipients to greater job prospects then this is the measuring stick that should be used to assess the success of workfare, argues Maeve Quaid.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".