Bibliographic record
Abstract
Font formats are a tug of war between artists (designers and drawers), programmers (computer scientists), the business world, and users. Each of these four groups has had an influence on the paththatfontformatshavefollowed. Wereviewthesuccessesandfailures,andpresentawishlist of properties that a good font format should have. Résumé Les formats defontes ont depuis toujoursétéuntir à la cordeentreles artistes(graphistes et dessinateurs de fontes), les programmeurs (informaticiens), le monde des affaires et les utilisateurs. Chacun parmi ces groupes a influencé l’itinéraire historique que les formats de fonte ont suivi ces vingt dernièresannées. Nousallons,danscetteprésentation,revoirlessuccèsetleséchecsdesformatsdefonte,etnous allons présenter une liste de vœux des propriétés que nous considérons qu’un bon format de fonte devrait avoir.
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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.137 | 0.060 |
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".