Comparing Government Social Welfare Service Acquisition Regimes: Marketisation and Bases for Competition in Canadian and English Homelessness
Bibliographic record
Abstract
Abstract When governments acquire third-party social welfare services (SWS), they create institutions of acquisition. The rules and practices that governments adopt define who is able to participate, on what basis, and how prices are determined. This paper conceptualizes the institutions of SWS acquisition, their variations, and implications, in order to contribute to a deeper understanding of the link between contracting and nonprofit commercialisation. Institutions of SWS acquisition include rules of entry, participation, and assessment. Resulting acquisition regimes can be marketised to a greater or lesser extent, and this is influential through its effect on nonprofit competition. Drawing on interviews with public servants and nonprofit staff, the paper compares acquisition regimes for homelessness services in England, a regime that closely resembles a market, and Canada, a regime which is not marketised. In contrast to their non-marketised counterparts, this paper finds that marketised SWS acquisition regimes create incentives for participants to reduce prices by loss-leading or ratcheting down service quality.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".