Exploring New (Digital) Publishing Practices with *Le Pressoir*
Bibliographic record
Abstract
As is well known, scientific publishing is a complex activity, and many constraints must be taken into account in order to address a range of issues, be they the structuration of the text, the inclusion of a critical apparatus, the sharing of texts during the revision and editing phases, and the dissemination of the final version via multiple platforms. Existing publishing chains allow for the management of texts, their production, editing and publication; they are systems that take these parameters into account by default. However, they remain dependent on specific software that is generally not well adapted to scientific publishing, or on technical infrastructures that are difficult to maintain and to use by SSH researchers, or that do not allow for experimentation and necessary rethinking on new editorial and epistemological models. What if, by modifying the publishing chains from more singular technical approaches, it is possible to consider new publishing practices? It is about rethinking the way books are edited and published through the editing of books in a university publisher series ("Parcours numériques" at the Presse de l’Université de Montréal). Several principles are adopted to reconfigure the publishing chain: multimodal publishing, modular factory, minimal computing and progressive enhancement. Furthermore, it is about what was adapted for a new publishing experiment (Les Ateliers de [sens public]) along the way.
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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.041 | 0.040 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".