Socially desirable responding does moderate personality scale validity both in experimental and in nonexperimental contexts.
Bibliographic record
Abstract
L'influence de la reponse socialement desirable sur la validite de la personnalite autodeclaree est examinee dans trois etudes portant sur 1 056 participants, des dimensions de la personnalite a cinq modeles factoriels et une mesure solide sur le plan psychometrique de la conduite strategique des relations. Les resultats indiquent que le trucage induit de facon experimentale genere des effets moderateurs de la validite extremement forts pour la conduite strategique des relations, mais que de tels effets sont modifies, tout en restant substantiels et importants, pour les variations qui se produisent naturellement dans les reponses socialement desirables. Des niveaux attenues d'importance pour la reponse socialement desirable se produisant naturellement en tant que moderateur peuvent etre lies a un manque de validite du concept pour la mesure de la conduite strategique des relations et a la reduction de l'efficacite statistique en raison de la petite taille de l'echantillon. L'auteur conclut que le rejet de la reponse socialement desirable en tant qu'enjeu pour la validite de la dimension de la personnalite autodeclaree est premature.
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 | 0.041 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".