Understanding "effective resource management" to support evidence informed policymaking
Bibliographic record
Abstract
This critical review is part of a doctoral thesis by publication which studies how evidence-informed policymaking is practiced inside government departments. Previous research indicates that these practices involve negotiating three normative positions around evidence: fidelity to science, policy legitimacy, and effective resource management. The first two positions are well documented in the academic literature but not the third. The aim of this review is therefore to address the question: how can we understand the effectiveness of the different ways policymakers manage the resources available to them in support of evidence informed policymaking? It will do this through a critical review of the literature, focusing on empirical studies of resource management by civil servants in central government departments in the UK and several other countries with comparable government bureaucracies (Australia, the USA, Canada, New Zealand and northern European countries). The review will be carried out between February and April 2022 and will be submitted for publication as a journal article by the end of June 2022.
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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 | 0.108 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| 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".