Inclusive Education Status through the Lenses of Teachers in Zambia
Bibliographic record
Abstract
This study was conducted to examine the status of inclusive education in Zambia, learning from teachers’ perspectives about how inclusive education is being implemented and the whether teachers receive adequate support to implement inclusive education to learners with disabilities. The study employed a concurrent mixed design approach in which both quantitative and qualitative data were generated and applied. Open and closed ended questionnaires were used to collect data from teachers that were upgrading their qualifications via distance education at Kwame Nkrumah University, University of Zambia and Chalimbana University. Findings indicated that Zambia practices partial inclusion in which only the mild and moderate learners with disabilities are included in classrooms. Inclusive education is understood by teachers in the context of disability and teachers reported that they did not receive adequate support to implement inclusive education effectively. It is recommended that the Government of the Republic of Zambia through the Ministry of General Education should focus on training teachers in inclusive education and its methodologies to meet the learning needs of learners from different circumstances
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".