Bibliographic record
Abstract
This collection of essays has an explanatory as well as a normative focus. On the one hand it tries to establish and clarify what it is that we know, as well as that which we don't know (at least very well), about the ways in which ‘security’ is thought about and promoted within diverse empirical contexts. Based on what we know, and recognizing what we don't know, this book shares some key concerns about how the advancement and protection of democratic values is being threatened or compromised by contemporary arrangements for security governance. In light of such worries, various theoretical and practical ideas for ways forward are argued, and in some cases vehemently so, by contributors to this volume. What we, as editors, hoped for in preparing this book was to provide more structure to the ‘friendly dialogue’ that has been occurring between those advancing different descriptions and explanations of what has been happening and/or those offering different assessments of what is at stake for the future of democracy and what to do about it. In reading the chapters herein it will become clear that there is more agreement about what has been happening than there is about what to do about it. None the less, there remain important differences in the ways in which scholars describe and explain contemporary developments, reflecting their use of different conceptual and analytical tools.
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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 | 0.001 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".