Measuring Shrinkage in the Welfare State: Forms of Privatization in a Canadian Health-Care Sector
Bibliographic record
Abstract
Abstract.There is a discussion in the literature about whether, to what extent, and in what ways the welfare state is retrenching or otherwise changing. Both the health policy literature and the broader policy studies literature have tended to focus on economic measures of privatization. This study tests the adequacy of the measures of public-private change proposed by Stoddart and Labelle (1984) by using them to track and analyze the sequence of policy changes in automobile legislation, workers' compensation and health that transformed Ontario's rehabilitation health sector from being almost entirely public in 1990 to being almost entirely private a decade later. It suggests adding what is called “allocative decision-making power” to indicators used to assess public-private change in order to more adequately capture transformations. Résumé.Assiste-t-on au déclin de l'État-providence ou à sa transformation? Quelle est l'ampleur du phénomène? Ce sont des questions qui ont été maintes fois examinées. Or, les analyses des politiques de santé ainsi que les études plus générales des politiques publiques ont tendance à se concentrer sur les mesures économiques de privatisation. La présente étude vise à tester la pertinence des mesures du changement public-privé proposées par Stoddart et Labelle (1984) en les appliquant à la série des changements de politiques en matière de réglementation automobile, d'indemnisation des travailleurs et de santé qui ont fait passer le secteur de la réadaptation du système de santé ontarien d'un statut presque entièrement public en 1990 à un statut presque entièrement privé une décennie plus tard. Cette étude propose d'ajouter la mesure de ce qu'on appelle “ la capacité à prendre des décisions d'allocation ” aux indicateurs traditionnels d'évaluation de l'équilibre public-privé, afin de mieux décrire les processus de changement.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".