How responsible are people for their employment situation? An application of the triangle model of responsibility.
Bibliographic record
Abstract
Dans le present article, nous mettons a l'epreuve l'hypothese selon laquelle la responsabilite percue d'une situation d'emploi individuelle est influencee par ses etats de service. Les participants ont juge dans quelle mesure un individu cible employe, sans emploi ou sous-employe etait responsable de ses etats de service. Nous avons predit que la cible de l'individu sous-employe serait jugee moins responsable que celle de l'individu employe ou sans emploi, parce que le sous-emploi produirait des indices qui ameneraient a penser a des facteurs autres que personnels concernant les etats de service. La description de l'individu cible variait les etats de service et etait construite conformement au modele du triangle de responsabilite (Schlenker, Britt, Pennington, Murphy et Doherty, 1994). Comme nous l'avons predit, des liens etroits entre les elements du triangle de responsabilite (prescription, identite et evenement) ont produit des jugements de responsabilite plus elevee que des liens moins etroits. Aussi, comme nous l'avons predit, la situation d'emploi des individus cibles influencait les jugements relatifs a la responsabilite, comme les cibles de l'individu employe et de l'individu sans emploi ont ete evaluees plus responsables de leurs etats de service que la cible de l'individu sous-employe. Les implications pour les orientations de recherches futures sont decrites.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".