Bibliographic record
Abstract
Jack Kerouac meurt en 1969 laissant une ?uvre litteraire qui fait de lui un des auteurs americains les plus importants du XXe siecle. Cependant, jusqu'a l'âge de six ans sa langue maternelle fut le francais et son pere lui repetait «Ti-Jean n'oublie jamais que tu es breton». Fort de cette tradition familiale, l'ecrivain a tente d'identifier son ancetre mais la mission etait difficile tant il avait seme son parcours de fausses pistes. Desireux d'aller au bout de cette quete, Patricia Dagier a traque le moindre indice dans les archives en France et au Quebec tandis qu'Herve Quemener a suivi la quete bretonne de l'ecrivain a travers sa vie et son ?uvre. Si Kerouac s'est approche au plus pres de la solution, il aura fallu le travail solide de ces deux passionnes pour en trouver la clef. Patricia Dagier est genealogiste. En 1999, apres trois annees de recherches intensives, elle a demasque l'ancetre breton de Jack Kerouac. Soucieuse de verite historique, elle poursuit depuis dix ans ses investigations dans les archives bretonnes, francaises et canadiennes. Herve Quemener est journaliste. Redacteur au quotidien Le Telegramme depuis 1972 puis redacteur en chef de Bretagne Magazine de 1998 a 2006, il se consacre aujourd'hui a l'ecriture.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".