Bibliographic record
Abstract
In the aftermath of an epidemic that has undermined established border-crossing practices in Europe and around the world, this volume proposes to rethink the uses and representations of the border based on the following question: who makes a cross-border space, who shapes it, who makes it workable? Citizens, governments, local elected officials? Au lendemain d’une épidémie qui a mis à mal les pratiques établies de franchissement des frontières en Europe et dans le monde, ce volume propose de repenser les usages et les représentations du transfrontalier à la lumière de la problématique suivante : qui fabrique le transfrontalier, qui le modèle, qui le rend viable ? Les citoyens, les gouvernements, les élus locaux ? Il n’existe pas, d’ailleurs, d’espace transfrontalier archétypal : il y a autant de pratiques du transfrontalier que de situations transfrontalières. Et il ne suffit pas de dire que le transfrontalier du Rhin Supérieur n’est pas le transfrontalier de la Grande Région Saar-Lor-Lux, ou que les usages du transfrontalier propres à la francophonie canadienne ne ressemblent guère aux pratiques du transfrontalier mises en place par l’État chinois : il faut aussi prendre en compte le fait que tout espace transfrontalier est travaillé et transformé en permanence par d’innombrables tensions politiques, économiques, commerciales, logistiques et culturelles. C’est pourquoi, pour penser théoriquement le transfrontalier, nous avons décidé de nous appuyer sur une série d’études de cas menées par des chercheurs venus d’horizons disciplinaires divers, de la littérature à la sociologie en passant par les sciences de l’information et de la communication, les sciences de l’éducation, ou encore l’archéologie.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.938 | 0.932 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".