Employee Voice Contexts and Teacher Retention in Remote Secondary Schools in Tanzania
Bibliographic record
Abstract
Retaining teachers in their work stations is influenced by many factors which are contextually explained. Teachers’ retention practices in Tanzania and most Sub-Saharan African (SSA) countries have been ineffective partly because of being monetary based. While ‘voicing’ is regarded as a more feasible strategy for retaining teachers in these countries, conditions which favour voicing over exiting a remote school particularly in the Tanzanian context have been not systematically mapped out. This article presents and discusses seven conditions, to include: empowering, listening and cooperative leadership; habitability; friendliness outside school environment; investment potentialities; a supportive and peaceful school working environment; life as a “challenge” mindset; as well as patriotism and profession commitment, which were found to favour voicing over exiting a remote school. The findings imply that there is a need to empower teachers to influence change and reforms that matter to them, increasing teachers sense of investment in schools they are posted and in the profession (social and financial capital), checking ‘who goes into the teaching profession and with what level of struggle’; improving school-level relationships including justice practices from leaders and management, positive co-workers exchanges; training teachers to become patriotic to the nation and be committed to the teaching profession; and the need to improve cooperation and understanding between schools and their surrounding communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".