Dictionaries and the Digital Revolution: A Focus on Users and Lexical Databases
Bibliographic record
Abstract
It was March 2012 in Paris: one of us was attending the annual Journée des dictionnaires organized by Jean Pruvost. Paul Bogaards was also there. During a break, we began discussing the upheavals in the dictionary world caused by the digital age. That was when the idea for this special issue of IJL was conceived. In August 2012, a number of people at the 15th Euralex Congress were approached about the project and, according to Paul, “the idea was enthusiastically received”. Paul Bogaards passed away only a few weeks later in October 2012, so naturally we wish to dedicate this special issue to him. Thank you to Anne Dykstra for making this issue possible. This digital revolution will take us from one universe to another, from paper dictionaries to digital dictionaries. The two editors responsible for this special issue, speaking from a professional standpoint of course, belong to one or the other of these universes. Monique C. Cormier specializes in historical lexicography: her medium is paper, at least for original versions. Terminology specialist Marie-Claude L’Homme deals primarily with digital media. We have extensive experience with both paper and digital media and agree on the need to understand our current intermediate state. We thus venture without regret and without fear.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.042 | 0.059 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".