Bibliographic record
Abstract
On July 14, 2002, a 25-year-old neo-Nazi named Maxime Brunerie drove a rented car from suburban Courcouronnes to Paris for the Bastille Day parade along the Champs Elysees. He carried a guitar case containing a .22 rifle he had recently purchased. The day before, on the British neo-Nazi website Combat 18 (18 signifying AH, the first and eighth letters of the alphabet, the initials of Adolph Hitler), Brunerie had posted a message: Watch television on Sunday, I'll be the star. At the parade site, Brunerie blended in with the crowd. As President Jacques Chirac's vehicle approached, Brunerie extracted the rifle from the guitar case and took aim. According to most accounts, he managed to get off one shot before the person in front of him, a 56-year-old Alsatian nurse named Jacques Weber, grabbed the barrel and wrested the gun from him. The wouldbe assassin was then wrestled to the ground by, among others, a French Canadian of Algerian descent, Mohamet Chelali. The following day, Chirac phoned Weber and Chelali to thank them. Interviewed in Le Monde, both men expressed no desire to be considered heroes. As Weber put it, Nothing could be more normal than to act like that under the circumstances. But to recover from the emotional shock of the
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.524 | 0.314 |
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".