Bibliographic record
Abstract
Democracy, Society and the Governance of Security , Jennifer Wood and Benoît Dupont, eds., Cambridge: Cambridge University Press, 2006, pp. 291. This edited volume engages the question of security from a critical perspective, with an emphasis on sociology and criminology. The various chapters and overall argument, however, should be of considerable interest to political scientists, especially those seeking a broader understanding of international and domestic security. Specifically, contributors suggest that security is no longer a state-centric concept and is now a by-product of a wide range of public and private interests, including governments, corporations and community-based organizations. The collection includes a diverse range of chapters examining transnational commercial security providers (Johnston), inter-agency anti-terrorist networks (Manning), external stakeholders and police organizations (Dupont), linkages between health and security (Burris), and “enclosed” security areas, such as gated communities and privately owned shopping centres (Crawford). As a unifying theme, the editors question the relevance of democratic values in this “pluralized field of delivery.” The goal is to examine these issues by “integrating explanatory and normative theory” (1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".