The Public Sector in an Age of Austerity
Bibliographic record
Abstract
Following the 2008 global financial crisis, Canada appeared to escape the austerity implemented elsewhere, but this was spin hiding the reality. A closer look reveals that the provinces – responsible for delivering essential public and social services such as education and healthcare – shouldered the burden. The Public Sector in an Age of Austerity examines public-sector austerity in the provinces and territories, specifically addressing how austerity was implemented, what forms austerity agendas took (from regressive taxes and new user fees to public-sector layoffs and privatization schemes), and what, if any, political responses resulted. Contributors focus on the period from 2007 to 2015, the global financial crisis and the period of fiscal consolidation that followed, while also providing a longer historical context – austerity is not a new phenomenon. A granular examination of each jurisdiction identifies how changing fiscal conditions have affected the delivery of public services and restructured public finances, highlighting the consequences such changes have had for public-sector workers and users of public services. The first book of its kind in Canada, The Public Sector in an Age of Austerity challenges conventional wisdom by showing that Canada did not escape post-crisis austerity, and that its recovery has been vastly overstated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".