Paper to be presented at the Canadian Political Science Association Annual Conference,
Bibliographic record
Abstract
New fiscal pressures, rising neo-conservatism, and a general desire to reinterpret social rights and responsibilities have led to the emergence of welfare-to-work policies in Canada and internationally. This paper seeks to understand welfare-to-work but, most importantly, its implementation. The paper begins with a look at the rich literature on the emergence of welfare-to-work, making an important distinction between insertion ’ models and ‘workfare ’ models. In either format, welfare-to-work indicates a marked shift away from social rights towards an emphasis on personal obligation and responsibility. While the ‘new welfare ’ has received considerable attention, the implementation of such policies is a neglected area. This paper argues that studying implementation is crucial to the outcomes of policy reform: policy is, effectively, remade on the ground level. Understanding the importance of street-level bureaucracy, the paper questions whether the shift in welfare is replicated in implementation and how it varies across systems and models. Utilizing qualitative data from two months of non-participant observation and over forty interviews with welfare-to-work participants and workers in Manitoba and Ontario, the paper argues that the interactions between workers and clients can greatly alter the focus and nature of significant pieces of welfare-to-work policies. The paper argues that discretion at the implementation level, mitigated by time, information and attitude, is crucial in understanding the impact of policy. It concludes by discussing the implications of the findings on the importance of discretion. The implications include positing a role for street-level bureaucrats in devising policy to ensure effective implementation and also questioning to what degree we can understand welfare reform in the past two decades without significant implementation data.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.279 | 0.032 |
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".