Strategies for Enhancing the Productivity of Secondary School Teachers in South West Region of Cameroon
Bibliographic record
Abstract
This study investigates strategies used by principals for enhancing the productivity of secondary school teachers in selected government secondary schools in Cameroon. Four major strategies were examined. These include motivation, conflict resolution, supervisory and communication strategies and the extent to which they influence teachers’ productivity. Four research questions and hypotheses guided the study. Questionnaire was used to collect data from 350 teachers selected from a population of 1400 teachers in government secondary schools in Fako Division of the South West Region of Cameroon. The multi-stage sampling technique was used to select teachers for the study. Results showed that, principals’ communication, conflict management, supervisory and motivation strategies influence the productivity of teachers in Government Secondary Schools. Of the four strategies examined, conflict management strategy was found to have more influence on the productivity of teachers. Principals’ strategies have a direct relationship with teachers’ productivity. Therefore, there is a possible correlation between principals’ leadership and management strategies, teachers’ productivity and school effectiveness. In addition, effective collaboration amongst teachers is necessary for teachers’ effectiveness. It is recommended that principals should put in strategies that will enhance effective communication, conflict management, motivation and supervision to improve on the productivity of teachers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".