The Factors that cause the dropout rate in Zimbabwean Urban Secondary Schools and Remedies
Bibliographic record
Abstract
The major purpose of this study was to investigate the factors which cause student dropout in a selected urban secondary school located in Bulawayo Metropolitan Province. The study was informed by the post-positivist and constructivist paradigms, utilising a combination of quantitative and qualitative data in a mixed method approach. Data was collected using closed-ended questionnaires, document analysis and semi-structured interview protocols. Sources of data, methodology and theories were triangulated to authenticate the data gathered. Respondents and interviewees/key informants were systematically and purposively sampled respectively. The main themes centred on the concept of student dropout, its causes and remedies. The study, therefore, unearthed that participants were conscious of the concept, the student/family, community and school level factors which cause it. The latter, though minimum threatened the internal efficiency of the Zimbabwean education system. An inclusive, holistic and relevant curriculum meant to cater for the diversified needs of the students was proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".