Nœuds ou champs ? Analyse de l'expertise internationale sur la criminalité transnationale organisée et le terrorisme
Bibliographic record
Abstract
In the particular context of the post–Cold War era, when military threat reconstructed itself in discourses around more diffuse phenomena like organised transnational crime and terrorism, civil servants from state administrations were called upon to participate in more or less formal meetings within the framework of regional and/or international institutions. These meetings intensified during the 1990s, which helped reinforce or create networks of actors at an international level. Analysis of these work groups, also called “expert groups”, presents a certain number of empirical and epistemological difficulties that are explored in this article. The objective is to shed light on one of these expert groups in particular and, using empirical data that are often difficult to collect, assess the pertinence of two theoretical approaches developed in the social sciences: field theory and nodal governance.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".