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Record W3174537000 · doi:10.51983/ijiss.2020.10.1.485

School Library Services as a Catalyst for the Better Basic Education in Nigeria

2020· article· en· W3174537000 on OpenAlexaff
Maria Edeole Ilori, Victor Segun Oluwafemi, Emmanuel Sunday Odusina

Bibliographic record

VenueIndian Journal of Information Sources and Services · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsRedeemer University
Fundersnot available
KeywordsSchool libraryBusinessPrimary educationPolitical scienceMedical educationPublic relationsLibrary scienceMedicineSociologyPedagogyComputer science

Abstract

fetched live from OpenAlex

This paper reviews existing literature on how school library services could lead to better basic educational system in Nigeria. Extent literature reviewed that there is poor state of library services to pre-primary, primary and post primary education institutions in Nigeria, providing learning resources is the primary and fundamental role of the school library. Education cannot be achieved without resources that will enhance learning activities. The study concluded that library resources and services are grossly inadequate in many primary and post primary schools in Nigeria and several factors have been attributed to this anomaly in our education system. However the study therefore recommends that the school library authorities should improve in creating more awareness on library services and programmes; the library management in collaboration with the school authorities should provide more fund to run the library effectively; the school management authorities should provide a befitting and well equipped building for the library.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0070.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.002

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 head0.007
GPT teacher head0.260
Teacher spread0.253 · 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 designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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