The Social Investment State and the Social Economy. The Politics of Quebec's Social Economy Turn, 1996-2015
Bibliographic record
Abstract
This dissertation argues that investigating the social economy is necessary to fully understand social investment politics and the specificity of the Quebec social model. Quebec has been structuring and promoting a social economy sector since the mid-1990s, launching social economy policies in areas such as child care, perinatal services, home support, social housing, and social insertion. No other provincial welfare state has taken a similar path. Why? Given their goal to address post-industrial social needs by increasing employment levels, Quebec’s social economy policies are here construed as social investment policies. Drawing from the comparative social investment literature and the Canadian federalism literature, I identify five hypotheses accounting for Quebec’s distinct trajectory: power resources, cross-class coalitions, learning, structural changes, and federalism. Using a process tracing methodology and relying on a variety of documentary sources and a unique data set of 77 interviews, I argue that the strength of Quebec’s Left in the mid-1990s, in combination with coalition engineering during the 1996 Economy and Employment Summit, are what chiefly account for Quebec’s social economy turn. The centre-Left PQ has proven to be significantly more committed to the social economy than the centre-Right PLQ. Moreover, it was actors from the Left, including the Quebec Women’s Federation and the CSN, who brought the issue of the social economy on the government’s agenda. The Social Economy Task Force, launched in prevision of the 1996 Summit, then skillfully engineered a Left-Right social economy coalition based on the idea that the social economy would infringe upon neither the public, nor the private sector, while creating jobs and addressing unmet social needs. The dissertation’s argument is nuanced, however. Although Quebec’s social economy policies were launched en bloc, they were not quite underpinned by exactly the same causes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".