Introduction: Procurement and Politics—The Defence Policy Consensus or Aligning Strategy and Policy Is Necessary But Not Automatic
Bibliographic record
Abstract
Abstract Large-scale military platform procurement is an essential but understudied component in policy and administrative studies. Procurement decisions in this area, which include major platforms and systems such as ships and aircraft, are very expensive and feature complex multi-actor and multi-year processes which can be highly conflictual. The extant administrative literature on the subject is of limited help: on the one hand, most procurement studies in public administration and public management focus on smaller, short-term, more routinized and less conflictual purchases. On the other hand, studies centred on military acquisitions tend to treat each major purchase as idiosyncratic. Hence, military procurement provides an excellent source of case studies to expand our knowledge and understanding of larger and more complex types of procurement processes. It allows us to draw lessons about successes and failures that will be relevant to similar expensive and large-scale purchases, such as railways, hydroelectric dams, highways and port development, while also drawing out the similarities and lessons for future defence purchases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".