De l’usage des traces en sciences de l’information et de la communication
Bibliographic record
Abstract
Après une thèse en Sorbonne portant sur l’application de la pensée de Pierre Bourdieu aux sciences de l’éducation, suivi de sa participation à la fondation de la recherche en communication des organisations dans les sciences de l’information et de la communication (SIC) françaises, Béatrice Galinon-Mélénec a inscrit ses recherches en anthropologie de la communication. Ce virage l’a conduite à fonder le paradigme de l’Homme-trace. Dans ce qui suit, Béatrice Galinon-Mélénec revient sur son parcours et explique comment elle a abouti à une anthroposémiotique de la trace qui s’appuie sur une perspective anthropologique et coconstructiviste pour proposer une lecture des signes en tant que conséquences d’interactions entre « corps-trace » et « réalité-trace ». Elle précise également sa définition de la trace en tant que conséquence et celle de l’indice, de la marque et de l’empreinte au regard de la trace. Enfin, la chercheure explique sa vision de la recherche et des méthodes d’enquête aptes à saisir la complexité du réel à travers des « conséquences-traces » diversement accessibles aux humains.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".