COMPUTERISATION OF RURAL SCHOOLS IN ZIMBABWE:CHALLENGES AND OPPORTUNITIES FOR SUSTAINABLE DEVELOPMENT (THE CASE OF CHIPINGE DISTRICT)
Bibliographic record
Abstract
In this paper we seek to explain the relevance of introducing Computer Studies in Zimbabwean rural schools as a means to reduce the access to Information Communication Technology (ICT) gap between rural and urban schools. We first acknowledge the efforts of various stakeholders in education in introducing the Information Communication Technology curriculum in rural schools in the last ten or so years as a commitment to bringing Science and Technology to the rural pupil. In addition, we further explore the progress that has been made by rural schools that received computers from the Head of State and Government over the years. In the process, however, we observe that most rural schools have not fully embraced the ICT curriculum owing to a number of challenges. Thus, we contend in this paper that most rural schools that received donated computers in Zimbabwe had not been capacitated to fully utilise the new technology for the benefit of pupils, teachers and the community. As a result, most of the gadgets have been lying idle in classrooms due to lack of either proper infrastructural facilities such as computer laboratories and electricity as well as lack of trained ICT teachers. In the final submission, we implore stakeholders in education to facilitate ICT development in rural schools in Zimbabwe so as to increase access, quality and equity in education for sustainable rural development in Southern Africa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".