Bibliographic record
Abstract
Ce texte introductif dresse le bilan du Colloque Archéo-Éthique qui s'est déroulé à Paris les 25 et 26 mai 2018. Il présente également les contributions de ce numéro spécial consacré à l'éthique en archéologie, qui rassemble les actes du colloque. L'ensemble des contributions a été classé en cinq grandes parties : « Quelles collaborations entre archéologues et populations locales? », « (Ré)appropriation ou instrumentalisation des recherches archéologiques? », « Quelles collaborations entre archéologues professionnels et passionnés du patrimoine (de l’amateur qualifié au pillard)? », « Les restes humains, des vestiges archéologiques pas comme les autres », « L’archéologie face à l’impératif de gestion : quelles conséquences sur notre pratique? ». Deux articles transversaux font office d'introduction et de conclusion au volume, qui est ouvert par cet éditorial, et clos par une conclusion des organisatrices du colloque.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".