Exogenous and Endogenous Impacts into Teachers’ Work Performance Sphere
Bibliographic record
Abstract
By this synopsis research which conveyed of findings to unfold mutual effect between teachers’ performance and incentive scheme and teachers’ personal competency, and principal leadership, and work motivation, by means of explanatory research in which ex facto method was ad hock model chosen because of classified as non-experiment. The grounds populations of research target were upon teachers of Public Senior High School around Medan city which the total about 1446 teachers, 241 teachers set out as sample apart. Though Ex post facto as a means to examine proposed hypothesis it was found significant influence between free variable teacher’s personal competence toward work motivation and teacher’s work performance as bound variable. Carried out with statistical descriptive analysis and inferential analysis. Alluded to inferential analysis by technically path coefficient analysis whereby direct contribution of: 1) incentive scheme toward work motivation was 41%, 2) teachers personal competency toward work motivation was 17%. 3) Principal leadership toward work motivation was 25%. 4) Incentive scheme toward teachers’ work performance 17%. 5) Principal leadership toward teachers’ work performance was 16%. 6) Work motivation toward teachers’ work performance was 39%.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".