Correlates of Teacher Transfer in Rural Schools: A Case Study of Rural Primary Schools in Choma District
Bibliographic record
Abstract
Inadequate teachers in rural schools of Zambia is a serious impediment toward equitable and universal access to education. The aim of the study was to determine correlates of human resource transfer in rural primary schools of Zambia in particularly focusing on Choma District of Southern Province. An explanatory factorial was employed enlisting 144 teachers from 10 schools in Choma. Data was analysed using SPSS version 22. Exploratory factor analysis was the main analytic technique. The main findings are that more than three quarters n = 97 (76.4%) of the teachers are unlikely to make a transfer request when compared to only a quarter n = 30 (23.6%) who would not. There is no significant association between likelihood of asking for a transfer based on gender, level of education and age of the teacher. One sample t tests result showed that males and females differed significantly in the asking behaviour. The standard acceptable score for propensity for asking for a transfer was set a priori at ≥ 45. Moreover, male teachers scored significantly higher (µ = 47) and were willing to remain in rural Choma when compared to females (µ = 45.1) who scored lower and were unwilling to remain in rural Choma. Out of fifteen factors that were analysed, only six factors were retained. The regression model shows 48% of the variation and transfer asking behaviour can be explained by the independent variables. The remaining 52 % of the variance is explained by other variables not included in this study. Based on the result the model is fair even though the model indicates that one regressor out of six do influence propensity of transfer asking behaviour in Choma as the p vales is ≤ 0.05 of the ideal α values. Key Words: Correlates, Teacher Transfer, Rural, Primary Schools. DOI: 10.7176/JEP/16-13-12 Publication date: December 30th 2025
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".