Lire la presse du XIX<sup>e</sup>siècle en contexte numérique: vers une nouvelle historiographique des objets médiatiques?
Bibliographic record
Abstract
Over the last ten years, libraries (national and municipal, as well as those affiliated to the universities) have launched major initiatives in digitization, which seek to recast the newspaper as a cultural object with its own particular heritage. A new corpus is emerging across the world, one that is easily accessible, and this emergence accounts in large part for the passion for research into the literary history of the press. Beginning with the experience of working on www.medias19.org, this article seeks to take stock of the new avenues of research which have emerged in the digital context. To what extent does digital technology, the new media through which we read the historical press, modify the ways in which researchers read and appropriate newspapers from the past? What new hypotheses can be formulated from the starting point that digitization and screen reading are not simply anodine gestures but rather promise to transform current approaches, ways of working, methods of collecting data, and means of establishing connections between bodies of work?
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".