Imperatives of Bilingual Sign Language in Language Development of Learners with Hearing Impairment in Nigeria
Bibliographic record
Abstract
Bilingual sign language has emerged as a critical tool for the linguistic, cognitive, and educational development of learners with hearing impairment, offering a pathway to equity and inclusion in diverse societies. This paper examines the imperatives of bilingual sign language in Nigeria, emphasizing its role in promoting language development, literacy outcomes, social integration, and communication competence among deaf learners. Anchored on Cummins’ Linguistic Interdependence Hypothesis, the paper highlights how a strong foundation in Nigerian Sign Language (NSL) can transfer to mastery of written English, thereby enabling academic progression and lifelong learning. While significant challenges persist including the lack of formal recognition of NSL, inadequate teacher training, insufficient learning resources, negative societal attitudes, and limited funding emerging opportunities and policy windows provide grounds for optimism. These include global disability rights frameworks, rising advocacy movements, the growth of digital learning tools, reforms in teacher education, and momentum toward inclusive education policies. The paper argues that the institutionalization of bilingual sign language in Nigerian schools is both a linguistic right and a developmental necessity. It concludes that by fostering cognitive growth, literacy advancement, social inclusion, and communicative competence; bilingual sign language can bridge the gap between deaf learners and their hearing peers, positioning them for meaningful participation in national development. The paper recommended amongst others that Non-Governmental Organizations should strengthen partnerships with organizations like the Nigerian National Association of the Deaf (NNAD) to combat stigma and promote societal acceptance of bilingual sign language as a tool for inclusion.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".