Research on the Influencing Factors of Job Stress of University Teachers ---- Take Changchun University of Science and Technology as an Example
Bibliographic record
Abstract
The paper selects 159 teachers of Changchun University of Science and Technology (CUST) by stratifi ed sampling method to perform questionnaire survey, determines five factors that influence the job stress of university teachers by principal component analysis. It also analyzes the influence of academic title, gender, age, education background, length of service and discipline difference on the job stress so as to guide universities to deal with teachers’ job stress. Key words : University teacher; Job stress; Job performance Resume Le present texte selectionne 159 professeurs de l’Universitede de la science et de la technologie de Changchun (CUST) par la methode d’echantillonnage stratifie pour effectuer enquete par questionnaire, determine cinq facteurs qui infl uencent le stress au travail des professeurs d’universite par l’analyse en composantes principales. Il analyse egalement l’influence du titre de formation, le sexe, l’âge, l’education de base, la duree de service et de la difference de discipline sur le stress au travail afi n de guider les universites a faire face au stress d’emploi des enseignants. Mots cles : Professeur d’universite; Le stress au travail; Le rendement au travail
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".