Sollicitation de participation à une enquête par courriel: Effet de la présence sociale de l'attrait physique du demandeur sur le taux de réponse.
Bibliographic record
Abstract
De nombreuses techniques d'influence permettent d'augmenter le taux de sondage a des enquetes mais ces techniques ont ete testees pour les enquetes en face-a-face ou par telephone. Peu d'etudes ont evalue les effets de techniques de soumission en communication mediatisee par ordinateur. La personnalisation de la relation entre sollicite et solliciteur ainsi que l'attrait physique du solliciteur sont des facteurs qui favorisent l'acceptation d'une requete en situation de face-a-face. Aussi, deux experiences ont ete menees dans deux contextes differents ou une sollicitation pour une enquete etait adressee par courriel. La presence versus absence de la photographie du demandeur accompagnant le courriel et l'attrait physique du demandeur ont ete utilisees comme variables independantes. Les resultats obtenus montrent que le taux de reponses augmente lorsque la photographie du solliciteur est presente mais diminue lorsque le solliciteur est de faible attrait physique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".