Dilemmas of Evaluation, Accountability and Politics: Contracting Out Social Services in Ontario
Bibliographic record
Abstract
The context of social services raises a host of daunting and crucial questions for the role of government. First, should social services be delivered directly by government or contracted out to private service providers? If contracting out, ought government to distinguish between for-profit institutions and non-profit institutions, and if so, why? On what standards or criteria should government measure the success of contracting out in the social services settings, and how should government ensure accountability of service providers in accordance with public standards and the protection of vulnerable individuals? Finally, what is the public interest in social service delivery and how can government best discharge its duties and obligations in that regard? As I hope to demonstrate through the analysis below, the appropriate role of government relates not to the delivery of social services but to ensuring and enhancing the quality, safety, fairness, accessibility, responsiveness, efficiency, equity and effectiveness of those services. This set of responsibilities entails the capacity of government (whether through legislation, regulation, policy or contract), to develop and implement standards of evaluation and accountability.
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 | 0.040 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.032 | 0.029 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| 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".