Policy termination in state-driven spheres: the role of inter-agency de-alignment
Bibliographic record
Abstract
Abstract Early studies of policy termination focused on identifying the drivers and barriers to ending programs, such as the emergence of fiscal and other exigencies, which can lead to policy and program cancellation, but also noted that such factors do not always lead to closure. More recent work has advanced thinking on the subject and acknowledged a wider range of possible policy choices beyond simple cancellation, including modest reform or termination of small parts of a policy and more substantial partial dismantling, and in so doing has altered the nature of the field. Both these literatures to date, however, have struggled to identify the underlying reasons for alternative courses of action, focusing almost exclusively on the relative strengths and balance of power of social coalitions promoting termination versus those supporting the status quo, which is at best indicative. This logic also fails to deal with terminations that occur in spheres where social actors are less prominent, which are the subject of this article. The article uses examples from defense policy and military spending in Canada to examine the rise, cancellation, resurrection, and downscaling of several interlinked major naval weapons programs over the period 1975–2025 in order to explore the dynamics present in state-driven terminations. It focuses particular attention on the role of changing government commitments to programs and requests desired by armed forces, highlighting program continuation when government-service visions remain aligned and termination or dismantling when they do not.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".