Ce que nous appelions « l’histoire des médias » : l’exercice de l’archéologie médiatique
Bibliographic record
Abstract
Wolfgang Ernst est l’une des figures de proue de l’archéologie médiatique, un courant de recherche qui a pris de l’ampleur depuis le début des années 2000 pour ses approches théoriques et méthodologiques nouvelles pour l’étude des médias. La principale contribution d’Ernst à l’archéologie médiatique dans les dernières années a été une vive critique de l’histoire. Dans cet entretien, Ernst propose de concevoir l’archéologie des médias comme un « exercice » pour les chercheurs, un mode d’attention qui permet d’isoler les éléments techno-logiques des médias. Alors qu’il aborde la question de l’humanisme, de la nature mathématique du langage numérique et celle des machines symboliques, Ernst offre ici quelques pistes pour mettre au travail l’approche qu’il propose et mieux comprendre les défis à venir du champ de l’archéologie médiatique.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".