Spending and strategic reviews: how do crises matter?
Bibliographic record
Abstract
Purpose This paper explores various ways in which crises and shocks are associated with spending and strategic reviews, as well as reform. Design/methodology/approach The approach taken this article is theoretical and conceptual: we delineate different kinds of spending and strategic reviews, show how crises and shocks might vary along several dimensions and infer several propositions how crises and reviews might relate to each other and reform, which can guide future empirical study. Findings While crises and shocks can precipitate spending and strategic reviews, the nature of reviews could vary significantly depending on the scale, severity and perceived time horizons of the crisis or shock and whether policymakers believe the governance environment has fundamentally shifted. Reviews can be variously more selective, comprehensive, shallow or more forward-looking, and possibly performative. Reform may not flow from reviews and could proceed without them. Research limitations/implications This study was theoretical and conceptual in nature; the resulting propositions should be the focus of comparative case-study research. Practical implications This study will provide practitioners with additional concepts and language for analyzing the nature of spending and strategic reviews, and enable practitioners to better analyze the experience of other jurisdictions when informing the design and learning from their own reviews. Originality/value This is the first theoretical exploration of how crises, shocks, reviews, and reform intersect.
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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 | 0.017 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".