Implementing defence policy: A benchmark-"lite":
Bibliographic record
Abstract
Most countries put significant amounts of time and effort in writing and issuing high-level policy documents. These are supposed to guide subsequent national defence efforts. But do they? And how do countries even try to ensure that they do? This paper reports on a benchmarking effort of how a few "best of breed†small- to medium-sized defence organisations (Australia, Canada, and New Zealand) deal with these issues. We find that most countries fail to link goals to resources and pay limited attention to specific and rigorous ex-ante or post-hoc evaluation, even when compared to their own national government-wide provisions. We do, however, observe a (modest) trend towards putting more specific goals and metrics in these documents that can be - and in a few rare cases were - tracked. The paper identifies 42 concrete policy "nuggets†- both "do's and don'ts†- that should be of interest to most defence policy planning/analysis communities. It ends with two recommendations that are in line with recent broader (nondefence) scholarship on the policy formulation-policy implementation gap: to put more rigorous emphasis on implementation (especially on achieving desired policy effects), but to do so increasingly in more experiential ("designâ€) ways, rather than in industrial-age bureaucratic ones ("PPBSâ€-systems). © 2019 Informa UK Limited, trading as Taylor & Francis Group.
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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 | 0.128 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.041 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".