La relation de confiance en relations publiques : vers un modèle d’adéquation contextuelle optimale
Bibliographic record
Abstract
Cet article expose les résultats d’une recherche de type qualitatif portant sur la relation de confiance entre relationnistes et parties prenantes. Lorsqu’un relationniste doit défendre un projet ayant des retombées potentiellement négatives (ou perçues comme telles) pour les parties prenantes, comment peut-il bâtir des relations de confiance avec celles-ci ? Au total, 40 entretiens semi-directifs ont été conduits auprès de relationnistes et de parties prenantes afin de déterminer les éléments constitutifs d’une relation de confiance en relations publiques. L’analyse des verbatim a permis de développer un modèle de relations de confiance en relations publiques : le modèle d’adéquation contextuelle optimale.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".