Les mondes du renseignement entre légitimation et contestation
Bibliographic record
Abstract
Des séquences de révélations concernant les pratiques des services de renseignement – dont celles d’Edward Snowden constituent les plus récentes –, suivies parfois de scandales, puis de régulations légales ou politiques nouvelles permettant à ces pratiques de se perpétuer, le plus souvent sans connaître de changements substantiels, apparaissent avec une étonnante régularité dans le temps et dans l’espace. Elles attestent que les activités de renseignement intérieur et extérieur constituent une dimension essentielle du gouvernement des sociétés complexes mais qu’elles doivent néanmoins se relégitimer périodiquement, notamment lorsqu’elles sont publiquement mises en question. Et c’est ici que le détour par les contestations constitue une entrée utile en ce qu’elles rendent visibles des processus de légitimation qui habituellement restent de l’ordre de l’implicite. Croisant des enquêtes empiriquement fondées portant sur des terrains historiques et contemporains, en France, en Suisse, au Canada et aux États-Unis, ce numéro double de Cultures & Conflits entend éclairer des aspects encore mal connus du fonctionnement de l’ordre social et politique en démocratie. The sequential ordering of revelations about the practices of intelligence agencies (including the most recent revelations of Edward Snowden) and the scandals that sometimes accompany them, followed by legal regulations or new policies allowing them to continue, most often without substantial changes, appear with astonishing regularity across time and space. This suggests that internal and external intelligence activities are an essential dimension in the governing of complex societies but must nevertheless periodically re-legitimate themselves, notably when they fall under public scrutiny. And it is precisely these instances of public contestation that provide a useful entry into understanding process of legitimization that typically remain in the realm of the implicit. Combining empirically grounded historical and contemporary studies on intelligence activities in France, Switzerland, Canada, and the United States, this Culture & Conflits double issue aims to shed light on little known aspects of how social and political order function in democratic contexts.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.092 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".