Bibliographic record
Abstract
L’information numérique est à l’évidence devenue un enjeu et un objet central du travail des services de renseignement. La plupart d’entre eux intègrent désormais dans leur activité routinière le recueil de données personnelles venant de multiples secteurs de la vie sociale d’un individu et de ses relations, ainsi que leur analyse. Mais ils le font de manière diverse selon leur ancienneté dans le métier, leurs capacités en termes de personnel, de moyens financiers et technologiques, et surtout selon leurs visions de ce qu’est l’activité de renseignement. À partir de l’étude des principaux services de neuf pays occidentaux (États-Unis, Grande-Bretagne, Canada, Australie, Nouvelle-Zélande, France, Allemagne, Espagne et Suède), cet article se propose de construire rigoureusement un espace transnational du renseignement. La mise en relation des positions et des discours de ces acteurs avec leurs pratiques et le sens qu’ils leur donnent permet de comprendre les homologies ou, au contraire, les différences irréductibles qui structurent ensuite les coopérations et les types d’échange de données.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".