Bibliographic record
Abstract
Les auteurs analysent tour à tour les contraintes algébriques et théoriques liées à l'utilisation par les géographes des algorithmes aujourd'hui traditionnels de l'écologie factorielle. Devant l'ambiguité fondamentale de la majorité des résultats, ils en viennent à la conclusion que la règle géographique doit constamment dominer et contrôler la règle mathématique, l'outil mathématique venant pourtant féconder le contrôle critique et donner alors de meilleurs résultats géographiques. La question de leur « optimalité » est alors discutée en se référant plus particulièrement à la méthode des itérations discriminatoires. Troisième thème de réflexion enfin : celui qui conduit les auteurs à introduire la notion d'indicateurs géographiques progressivement découverts par les itérations factorielles et discriminatoires, indicateurs simples mais assortis d'un véritable arbre généalogique factoriel.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".