Affects et émotions numériques : matérialité(s) et instrumentalisation(s)
Bibliographic record
Abstract
Si les travaux de recherche sur les affects numériques sont affaire de tension, ce numéro de la revue Communiquer entend proposer des lignes tendues par les auteurs entre les nombreuses approches théoriques et méthodologiques pour l’analyse des affects numériques. Faisant suite à un colloque lors du 86e Congrès de l’ACFAS (2018) et à un dossier de la Revue française des Sciences de l’Information et de la Communication (2017), ce numéro interroge ces tensions à partir de terrains et de corpus originaux. La variété de ces terrains, des objets analysés, des champs disciplinaires et des auteurs convoqués, mais aussi des méthodes mobilisées, s’inscrit dans une volonté de montrer toute la richesse et les potentialités des études affectives numériques pour la communication (et au-delà). Pris dans leur ensemble, ils permettent de dégager les différentes composantes de l’affect, cumulant intensité, orientation et valeur. If research on digital affects are a matter of tension, this issue of the journal Communiquer intends to propose lines tense by the authors between different theoretical and methodological approaches for the analysis of digital affects and emotions. Following a conference at the 86th Congress of ACFAS (2018) and an issue published in Revue française des Sciences de l'Information et de la Communication (2017), this issue questions these tensions from original fields and corpus. The diversity of these fields, analyzed objects, methods, disciplinary fields and invited authors is part of a desire to exhibit the richness and potential of digital affects studies for communication. Taken as a whole, the presented articles allow the identification of the different components of affect et emotion.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".