Supporting deaf learners in Nepal via Sustainable Development Goal 4: Inclusive and equitable quality education in sign languages
Bibliographic record
Abstract
PURPOSE: To highlight the World Federation of the Deaf's (WFD) development of inclusive education policies for deaf learners as part of the community of people with communication disability. These policies were developed alongside and included baseline data collection regarding deaf education in Nepal. RESULT: The commentary will report on sustainable partnership practices, the development of governmental accountability measurements, and data gathered during fieldwork in Nepal to provide evidence regarding deaf people's access to inclusive and equitable quality education in sign language. CONCLUSION: The recommendations from this project focus on enhanced access, policy, and training to ensure a free education in sign language for all deaf learners and their families in low-income countries using Nepal as a model. Existing bilingual settings in sign languages should be considered as part of a national inclusive education system, and this is in keeping with the views of deaf organisations in Nepal. Free education in sign language for all deaf learners and their families is an integral and wholly attainable part of inclusive education systems. This commentary focuses on good health and well-being (SDG 3), quality education (SDG 4), peace, justice and strong institutions (SDG 16) and partnerships for the goals (SDG 17).
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".