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Record W3134812930 · doi:10.29173/iasl7914

Promotion of reading habit among school children in Sri Lanka

2021· article· en· W3134812930 on OpenAlexvenueno aff
Pradeepa Wijetunge

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)Reading (process)Sri lankaVariety (cybernetics)Mathematics educationScale (ratio)PedagogyPolitical sciencePsychologySociologyPublic relationsComputer scienceGeographyPoliticsSocioeconomics

Abstract

fetched live from OpenAlex

School Library Development in Sri Lanka is a large scale project which covers 4000 schools and includes building construction, distribution of books, furniture and equipment and training of human resources funded by the World Bank. BOBLEP (Book Based Language Enrichment Programme) developed from the concept of reading promotion within the library project. The project not only promotes reading using the purchased books, but it also promotes production of books by school children as well as teachers. It was decided as a result of the success of the project, to expand similar reading promotion activities in general. Teacher and Teacher Librarian education programmes of Sri Lanka incorporated a variety of such activities to train reading promotion among school children. The full paper will present the history and the structure of the project and activities carried out by the teacher librarians to expand it from an English language project to a reading promotion project conducted by the school libraries. It is expected that by sharing the information of this project, other developing countries which face similar constraints in providing suitable reading material can gain useful ideas.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.287
Teacher spread0.264 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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