Bibliographic record
Abstract
Chronique des ArchivesCes quelques lignes n'ont d'autre ambition que de donner un état de ce qui se fait dans les diférents services d'archives de la région qui ont bien voulu répondre, et que je remercie ici (pour les archives départementales du Bas-Rhin Pascale Verdier, pour Erstein Vincent Husser, pour Haguenau Michel Traband, pour Sélestat Hubert Meyer, pour Strasbourg Laurence Perry, pour Colmar Francis Lichtlé, pour Guebwiller Sophie Coignot-Genin, pour Illzach Agathe Antony, pour Saint-Louis Sylvie Meyer, pour Soultzmatt Céline Frank, pour Turckheim Florent Edel).Indiquant les principales entrées, elles constituent aussi une source précieuse sur la localisation de fonds qui ne seront accessibles qu'une fois classés.Le chercheur consultera avec intérêt les diférents sites Internet existant où il pourra trouver instruments de recherche (répertoires, inventaires, bordereaux de versement par exemple) et parfois documents en ligne.Il pourra ainsi au moins préparer ses visites voire faire certaines de ses recherches chez lui.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.136 | 0.057 |
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".