Teaching and Supporting Students with Intellectual Disabilities in Democratic Republic of Congo
Bibliographic record
Abstract
Abstract Children with intellectual disabilities (ID) often experience challenges due to low learning expectations and educational quality and limited educational access. Often, in locations impacted by higher levels of poverty, such as Kinshasa, Democratic Republic of the Congo, educational disparities for children with ID are even more pronounced. To date, limited research has been conducted about educating children with ID from a teachers' perspective in Kinshasa. In this exploratory study, we analyzed data from 23 interviews with special education professionals who work with students with ID to describe their professional goals and explain the experience of delivering education to children with ID in Kinshasa. Most participants were drawn to their profession out of compassion and expressed the hope that they would help their students find jobs, live independently, and become valued members of society. The participants spoke in a uniform voice about how their passion and goals for their students were dampened by the difficulties they faced in educating their students, including low pay and the lack of a comprehensive system to support their work. Addressing these barriers is an important step towards ensuring the full personhood for all students in DRC, including children with ID.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".