MétaCan
Menu
Back to cohort
Record W3041404001 · doi:10.29173/slw8243

ICT Facilities and Literacy in Rural Non-Government Secondary School Libraries of Bangladesh

2021· article· en· W3041404001 on OpenAlexvenueno aff
Zakir Hossain, Yasmine Hashmi, Muhammad Mezbah-ul-Islan

Bibliographic record

VenueSchool Libraries Worldwide · 2021
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyGovernment (linguistics)LiteracyInformation literacyWork (physics)Rural areaExploratory researchSchool libraryMedical educationPublic relationsPedagogyPolitical scienceSociologyLibrary scienceEngineeringComputer scienceMedicineWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

The study explored the ICT facilities and services at rural Non-Government Secondary (NGS) school libraries in Bangladesh. It identified the ICT literacy of NGS school library professionals known as assistant librarians. An exploratory method was used to ensure the best possible outcomes. Based on the interview and literature review in the qualitative phase, a questionnaire was developed for the quantitative phase and collected 86 responses using an online survey. Findings showed, most of the rural NGS school libraries do not have the ICT facilities and of those that do, they are insufficient in number to provide efficient services to library users. There was a lack of ICT skills among assistant librarians and most agreed that ICT literacy would increase the efficiency of their work and regarded it as an essential tool for school libraries. The study provides an analysis of the prevailing situation that helps in planning for policy makers and further in-depth research.

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)0.001
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.008
GPT teacher head0.213
Teacher spread0.204 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSchool Libraries WorldwideSame topicICT in Developing CommunitiesFrench-language works237,207