Standardisation et différenciation des emplois des emoji sur Facebook : observations à partir d’un exercice pédagogique en DUT
Bibliographic record
Abstract
S’appuyant sur un examen en contexte des emoji d’un corpus de pages Facebook francophones d’octobre 2018, cet article montre qu’une grammaire des emoji peut être dégagée de l’usage qu’en font les community managers. Ces outils présentent un intérêt rhétorique et marketing en tant qu’ils visent à capter l’attention du lecteur, à susciter un engagement de sa part et à renforcer sa fidélité à la marque. Ce travail émotionnel et affectif, selon la terminologie établie par Camille Alloing et Julien Pierre, est le corollaire d’un processus de normalisation par lequel des principes tacites d’utilisation des emoji s’imposent peu à peu. En dépit de cette codification progressive, des espaces de jeu offrent la possibilité de créer un répertoire singulier et adapté aux spécificités des marques et à leur stratégie de différenciation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".