Utilization of Assistive Technology for Effective School Library Service Delivery to Students with Disabilities in Nigeria
Bibliographic record
Abstract
Disability is a part of the human condition. Everyone might experience disability either permanently or temporarily at some point in their life. It was estimated that about 25 million people live with one form of disability or more. This study explored the utilization of assistive technology for effective school library service delivery to students with disabilities in Nigeria. The study adopted descriptive survey design and used online structured questionnaires to collect data from school librarians. A total of 240 school librarians responded to the online survey across the 36 states in Nigeria. Data collected through online questionnaires were analyzed using percentage, mean and standard deviation. The study revealed that, there is inadequate availability of assistive technologies for effective school library service delivery to students with disabilities in Nigeria. Also, the extent of utilization of assistive technologies for effective school library service delivery was very low except for braille technology and Hearing Aids. The major challenges associated with the utilization of assistive technology for effective school library service delivery to students with disabilities in Nigeria are lack of support from the government, inadequate assistive technology in the school libraries, ranked together with inadequate funding of the school libraries; and unstable electric power supply. The study recommended; Provision of adequate assistive technology in the school libraries by the government. Training of both students with disabilities and school librarians in the use of assistive technology. Developing adequate interest in inclusive school library service by the government. Adequate funding of the school libraries by the government. Adequate support from the government through provision of material and human resources. Provision of adequate support service on the use of assistive technology by the manufacturers or vendors. Provision of stable electric power supply through installing solar power systems or purchasing standby generators.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".