Teachers’ Social Goal Orientations with the Job Demands-Resources Model: Data and Analysis Files
Bibliographic record
Abstract
These files contain the anonymized data used to create the tables and figures found in "Exploring Teachers’ Social Goal Orientations with the Job Demands-Resources Model". Please refer to the abstract for the paper below: Positive teacher-student relationships are recognized as critical social resources for promoting dyadic well-being and adaptive instruction. However, there is scarce research on the relationship between teachers’ motivation for connecting with students and teachers’ psychological adjustment. To explore the relationship, this study applied the job demands-resources model to investigate how teachers’ social goal orientations corresponded with their well-being, job satisfaction, and school commitment, with burnout and work engagement as mediating variables. Whereas teachers’ social mastery-approach goals (attempting to improve student relationship-building competencies) were expected to serve as beneficial psychological resources, their social work-avoidance goals (putting minimum effort into student relationships) were hypothesized to function as would detrimental occupational demands. Findings from mediational structural equation modeling of questionnaire responses from Canadian K-12 teachers (N = 154) showed teachers with stronger social mastery-approach goals to be more engaged and, in turn, report greater well-being, job satisfaction, and school commitment. In contrast, teachers with stronger social work-avoidance goals reported less work engagement that was further associated with lower well-being and job satisfaction, as well as greater burnout and, in turn, poorer job satisfaction. Implications concerning teachers’ interpersonal appraisals and psychological health initiatives were discussed.
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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.188 | 0.042 |
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".