Bibliographic record
Abstract
Policy termination is an underexplored area in policy studies, gaining attention during the 1980s with the rise of new public management and austerity measures. Assumptions of rational, evidence-based evaluations quickly gave way to the conclusion that political ideology and partisanship are the central drivers of termination in policy research, but with little insight into how and why. The recent upsurge in populist discourse has renewed interest in policy termination, particularly as populist agendas frequently include rhetoric about dismantling government programmes. This article examines how ideas, in the form of populist discourses, influence policy termination. Using the Ontario Progressive Conservative Party’s (OPCP) 2018 election as a case study, it focuses on the termination of Ontario’s carbon cap-and-trade policy and the repeal of its sexual health education curriculum. It highlights the role of political ideas and discourse in reframing issues and providing compelling narratives to build broad supporting coalitions and lower barriers to termination. The findings suggest that while populist leaders can mobilize support for termination, the success of such efforts depends on the alignment of political ideas with the lived realities and values of the people. This article contributes to the literature by elucidating the mechanisms through which ideas influence policy termination, offering insights into the dynamics of policy change in the context of contemporary populism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.022 | 0.075 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".