Influence of Perceived Teacher Identity on Job Satisfaction among Elementary School Teachers: Focusing on the Mediating Effect of Teacher Commitment
Bibliographic record
Abstract
This study is to analyze the relationship between elementary school teacher identity perception, job satisfaction, and teacher commitment. To this end, the influence of elementary school teacher identity perception on job satisfaction and the mediating effect model of teacher commitment were established, and the variables of teacher identity perception, job satisfaction, and teacher commitment were measured for 203 elementary school teachers in 5 regions of Korea. The analysis verified the influence and mediating effect among variables by applying structure equation modeling. The analysis results are as follows. First, the elementary school teacher identity perception has a positive impact on job satisfaction and teacher commitment. Second, teacher commitment has a positive impact on job satisfaction. Third, teacher commitment has a partial mediating effect between the elementary school teacher identity perception and job satisfaction. Therefore, it is necessary to consider the variable of teacher identity perception in the decision-making situation for policies and systems to increase the job satisfaction and teacher commitment of elementary school teachers. In a situation where educational innovation and reform are being promoted worldwide along with the 4th industrial revolution, teacher identity perception will act as an important factor in the success of these changes and innovation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".