Prevalence of Professional Misconduct in Nzega District, Tanzania Public Secondary Schools
Bibliographic record
Abstract
The purpose of this study was to investigate the prevalence of professional misconduct among public secondary school teachers in Nzega District, Tanzania public secondary schools. This study employed descriptive survey research design. The sample consisted of 403 respondents in which teachers and students were randomly selected, while heads of schools and Teachers Services Department (TSD) officials were purposively selected based on their administrative roles. Data for this study were collected through questionnaire and semi-structured interview guide. Data were analyzed using descriptive statistics using SPSS version 20. Findings revealed that teachers’ professional misconduct was low. However, financial mismanagement, negligence of duty, and absenteeism were among the common professional misconduct acts in secondary schools in Nzega District. Findings also revealed that, poor remuneration, failure to fulfill teachers’ needs, and lack of motivation were among the sources of teacher’s explanations for misconduct. Based on the findings, the government through the Ministry of Education and Vocational Training (MoEVC) should respond to teachers’ needs in a timely manner, and conduct regular seminars on teacher professionalism. Moreover, a similar study should be conducted to assess teachers’ misconduct at primary school and higher institution levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".