WHEN POLICY INTENTIONS GO ASTRAY: THE DEPLOYMENT OF HOME CARE AND HOME SUPPORT MARKET TOOLS IN FRANCE AND QUEBEC
Bibliographic record
Abstract
Abstract Using Gingrich’s (2011) typology of welfare markets, this contribution studies the selection and enactment of market tools in two different jurisdictions (France and Quebec). Quebec represents a classic example of a managed market with regional health authorities (CISSS/CIUSSS) seeking the lower the cost of long-term care by contracting services to community groups and the private sector. These contractual agents have gradually replaced health and social care professionals within the public system. France has opted to enact a consumer driven market with the introduction of the allocation personnalisée d’autonomie (APA) which provides cash benefits to eligible older adults who can then select the provider of their choice. Both of these market reforms are compared and analyzed across three types of territories : rural (Finistère and Bas-St-Laurent), urban (Paris and Montreal), and in industrial decline (Somme and Mauricie). Despite operating within highly differentiated long-term care policy frameworks and market tools, both jurisdictions face similar difficulties in the enactment of market mechanisms. For instance, rural territories fail to generate sufficient providers to develop a market where a regional health authority (Quebec) or older adults (France) can actually select a provider and negotiate terms of service. Public authorities must then intervene to generate market-like conditions. Regardless of the jurisdiction, the conceptualization of market mechanism has clearly an urban setting in mind making them ill-suited for other environment and there is gradual movement towards the development of austerity market where individuals are forced to seek alternatives beyond the market tools deployed by governments.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".