Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Comment expliquer un attentat dans un cinema aux Etats-Unis, dans une mosquee a Quebec ou sur une promenade a Nice, en France ? Quelles sont les motivations qui poussent certains individus a commettre des actes aussi odieux que violents ? Comme une chimere, le terrorisme prend plusieurs visages en exploitant, entre autres outils, les reseaux sociaux. Mais en faisant des amalgames douteux associant radicalisme, islam et terrorisme, on occulte dangereusement les veritables causes de la violence politique pratiquee par plusieurs groupes extremistes ou par des loups solitaires… Une telle confusion entraine necessairement des rates dans la lutte contre le terrorisme, qui risque helas d’etre une bataille sans fin. La science politique apporte des eclairages utiles et essentiels a ce probleme. Elle permet de mieux cerner les interets geopolitiques obscurs qui se cachent derriere ces tragedies repetees.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it