Argo : Comment la CIA et Hollywood ont imaginé la plus audacieuse mission de sauvetage de tous les temps
Bibliographic record
Abstract
Le 4 novembre 1979, des etudiants iraniens prennent d’assaut l’ambassade americaine a Teheran et retiennent en otages des dizaines de fonctionnaires et diplomates americains. Six d’entre eux parviennent a fuir et trouvent refuge a l’ambassade du Canada. Ils reussissent a contacter leur gouvernement, et la CIA decide de monter une operation d’envergure pour les exfiltrer du pays. A la tete de l’operation, Tony Mendez, un agent chevronne de la CIA, qui imagine de tourner en Iran un film de science-fiction intitule Argo. Il se rend a Teheran au pretexte de trouver le decor ideal et visiter les lieux de tournage… En janvier 2000, apres de nombreuses peripeties et sueurs froides, il parvient a faire monter les six Americains dans un avion. Direction : les Etats-Unis, la liberte. Dans ce document qui a servi de base au film de Ben Affleck, Tony Mendez donne tous les details et devoile les dessous de l’operation extremement complexe et dangereuse qu’il a menee a bien.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".