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Record W2979948815 · doi:10.29173/iasl7405

Motivating Teachers to Use the School Library

2019· article· en· W2979948815 on OpenAlexvenueno aff
Josip Rihtarić

Bibliographic record

VenueIASL Annual Conference Proceedings · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsSchool libraryPresentation (obstetrics)School teachersWork (physics)Order (exchange)Mathematics educationSociologyPedagogyLibrary sciencePsychologyMedical educationComputer scienceEngineeringMedicineBusiness

Abstract

fetched live from OpenAlex

The participants will be introduced with several procedures a school librarian can motivate teachers to use the school library. Some of the procedures can be applied globally in all school libraries, while others are specific to Croatia, or countries with a similar education system. Teachers are not the most numerous, but they are the most important users of school library. When teachers support the work of school librarian, he or she can fully accomplish his or her mission. If the support from teachers is missing, school librarian can employ different strategies to motivate students to use the school library, but only with a limited success. In order to motivate teachers to use the school library and to encourage the cooperation between them, the school librarian and the teachers, the librarian can initiate several activities, some of which are to be presented. The inspiration for these activities was James Henri’s presentation at the IASL conference in Italy in 2009.

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)0.007
metaresearch head score (Gemma)0.019
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.007

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 head0.268
Teacher spread0.245 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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