Creating La Vie littéraire au Québec in the Digital Era
Bibliographic record
Abstract
Presenting the work that, for nearly thirty years, our team conducted on “La vie littéraire au Québec” (a series of volumes published on literary life in Quebec) is an opportunity to observe the practical aspects of the introduction of digital tools in a history of literature/cultural history research. In this paper, we discuss the modalities of the digital turn undertaken five years ago in our team as we emphasize the epistemological bases of our project and the resulting structure of the books. This turn was part of the modernization of the team’s tools and working methods, of course, but we want to focus here on the behind-the-scene and self-reflexive aspects of this digital shift, through the examination of three of its most qualitative results. The first one is the improvement of the observations that digitalization allows, thanks to the increasing quantity of material that it is now possible to consider and study; the second one is the possibility to initiate an efficient collective upstream work that enables deeper analyses; the third one is the ability to store, transfer and recycle data, which participates in a crucial and often under-estimated way in the advancement of scientific work. We finally hope that our discussion will contribute to a better understanding of what is at stake with digital data – far from a genuine digital “revolution,” it seems to us that digitalization in the history of literature is just the next step in the continuity of the Annales School.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".