Ontario Pension Policy Making and the Politics of CPP Reform, 1963–2016
Bibliographic record
Abstract
Abstract After years of pension policy drift in a broader context of global austerity, the Canada Pension Plan (CPP) was enhanced for the first time in 2016 to expand benefits for Canadian workers. This article examines Ontario's central role in these reforms. The deteriorating condition of workplace plans, coupled with rising retirement income insecurity across the province's labour force, generated new sources of negative feedback at the provincial level, fuelling Ontario's campaign for CPP reform beginning in the late 2000s. The political limits of policy drift and layering at the provincial level is considered in relationship to policy making at the national level. As shown, a new period of pension politics emerged in Canada after 2009, in which the historical legacy of CPP's joint governance structure led to a dynamic of “collusive benchmarking,” shaped in large part by political efforts of the Ontario government, leading to CPP enhancement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".