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Record W3177825458 · doi:10.1177/14782103211030145

A preliminary study on visually impaired students in Bangladesh during the COVID-19 pandemic

2021· article· en· W3177825458 on OpenAlexaff
Saifullah Mahfuz, Md. Nazmus Sakib, Matt M. Husain

Bibliographic record

VenuePolicy Futures in Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPandemicGovernment (linguistics)Coronavirus disease 2019 (COVID-19)Coping (psychology)Higher educationPhoneData collectionQualitative researchPublic institutionEconomic growthQualitative propertyPsychologyPrivate sectorSociologyPolitical scienceMedical educationPublic relationsSocial scienceMedicineEconomics

Abstract

fetched live from OpenAlex

This article problematizes the status of the visually impaired students in Bangladesh under the COVID-19 global pandemic. We inquire into two inter-related questions: (a) what level and quality of technological access does a visually impaired student have in their higher education institution (e.g. a university or government-affiliated college operating under a university)? And, (b) how are these students coping academically under the pandemic? Our preliminary study employed mixed methods for data collection, encompassing a quantitative survey questionnaire followed by qualitative phone interviews. We reached out to approximately 15 male and female students enrolled in public and private higher educational institutions in the country. The findings will be instrumental to initiate a collaborative discussion among academics and practitioners in the government, non-government and private sectors in the country and around the Global South.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.049
GPT teacher head0.451
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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