Adaptive Facilities for Supporting Differently-abled Persons in the Library Environment: A Case Study of Libraries in Shillong, Meghalaya, India
Bibliographic record
Abstract
The differently-abled are emerging as a force that cannot be ignored today. While the capabilities of many of these are almost at par with and sometimes even surpass those of the 'abled', their special needs cannot also be denied. Public institutions of all kinds have geared up or are in the process of gearing up to facilitate the smooth movement and functioning of this group, with a variety of facilities, from general ones such as crutches, elevators, ramps, braille, hearing aids etc. to more specialized ones such as CUPID, Dexter etc. Libraries worldwide have also put many of these adaptive facilities in place. This study set out to explore whether libraries in Shillong, North East India, provide adaptive facilities for supporting the differently-abled. The study found that the libraries in Shillong are grossly under-equipped in this regard and that excuses abound. Reasons for not having adaptive facilities for the differently-abled range from lack of funds to 'absence of differently-abled patrons'- a claim that exposes the lack of planning on the part of the libraries. It was found however, that some Library staff members do realize the significance of the differently-abled and the need for facilitating their functioning in the Library environment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".