Intercorporéité artefactée, entre réification et personnification
Bibliographic record
Abstract
Qu’est-ce que la presence aujourd’hui ? Comment adapter et s’approprier les nouvelles formes de presence dans un contexte de recherche ? Comment former les futur·e·s chercheur·e·s a distance ? Quelles sont les implications de l’environnement numerique sur le travail d’equipe ? A l’heure ou la visioconference devient norme, ou la plupart de nos echanges ont lieu a travers un ecran, cet ouvrage propose un ensemble de reponses – une boite a outils conceptuels – adaptees aux nouvelles realites et experiences de la vie de chercheur·e·s. Ces concepts et methodes ici crees sont applicables a l’etude de toute situation d’interaction par ecran, formelle et informelle (contexte professionnel, institutionnel, interpersonnel, etc.). Avec une approche interdisciplinaire, il etudie les differents aspects des interactions par ecran et analyse les effets des artefacts (robots de telepresence) et des plateformes de visioconference dans un contexte hybride melant presence et distance : co-construction de l’attention, emergence d’une intercorporeite, nouvelles normes de politesse, effets de presence, gestion des bugs et des rates, impact sur la formation des doctorant·e·s… Les auteur·e·s, en se choisissant comme propre objet d’analyse, posent ici les bases d’une ethologie reflexive visuelle placant la multimodalite, le vecu subjectif et la sensorialite au cœur de l’analyse des interactions, de l’observation a la diffusion scientifiques. En coherence avec l’objet du livre, son format et son mode de production ont permis l’experimentation d’un nouvel ecosysteme de publication base sur des environnements de travail collaboratif a distance et sur la libre diffusion du savoir.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".