A systematic review of literature on inclusive education with special emphasis on children with disability in Pakistan
Bibliographic record
Abstract
Enabling inclusive education and eliminating all differences in education is a key area of Sustainable Development Goals. This systematic review of the literature analysed the major challenges regarding inclusive education in Pakistan. It also studied how these have been investigated by different researchers across the world in the context of a developing country, i.e. Pakistan. The databases searched for carrying out the systematic review were Google Scholar, ERIC, Education Source, ProQuest, JSTOR, Springer, Project Muse, and Informs. The exploration included peer-reviewed research papers from 1980 to 2022 on inclusive education. Approximately 30 research articles were analysed as these met the inclusion criteria. The key findings indicated that global and national policy guidelines are not implemented regarding inclusive education. Moreover, educational institutions in Pakistan are not prepared to cater to children with special educational needs. In addition, the lack of collaboration among all stakeholders and the lack of professionally developed teachers are major challenges towards including these learners. It is recommended that some practical measures need to be taken to implement global and national policy guidelines by the government. To carry out effective implementation of inclusive education collaboration with all stakeholders is recommended.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".