Assessing the effect of home-to-school distance on student dropout rate in Adi-Keyih sub-zone, Eritrea
Bibliographic record
Abstract
This study assessing the effect of home-to-school distance on student's dropout rate in Adi-Keyih sub-zone, Southern administrative region, Eritrea. In the current study, correlational method is used to test the significance of home-to-school distance on dropout rate of students. The population of the study embraces all 24 schools in Adi-Keyih sub-zone and their 15,457 students. Out of the total students there were 1215 dropout students (7.9 %) and all of them have been included in the study. For comparative and inferential purposes, the same number of non-dropout students (1215) were selected using systematic random sampling methods and the sample of non-dropout students from each school is proportional to the active student population in each school. This approach yielded a total of 2430 students, which is 15.7 % of the total population. Data were collected from student's personal files by conducting field visits to each school and analysed using simple Chi-Square and logistic model. The finding of logistic regression analyses show that home-to-school distance has a direct effect on dropout rate: as home-to-school distance increases, the likelihood for a dropping out also increases. The relationship is statistically significant at P < 0.10. The study clearly demonstrates that home-to-school distance affect the dropout rate in Adi-Keyih sub-zone, but this result could not be generalized across the whole country as it requires a bigger and more detailed study.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".