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Record W2765299391 · doi:10.29173/iasl7199

Café in a school library: to strengthen links with school and society.

2016· article· en· W2765299391 on OpenAlexvenueno aff
Yuriko Matsuda

Bibliographic record

VenueIASL Annual Conference Proceedings · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsSchool libraryInformation literacyPrincipal (computer security)LiteracySociologyPoint (geometry)Value (mathematics)School educationPedagogyMathematics educationMedical educationLibrary sciencePsychologyMedicineComputer science

Abstract

fetched live from OpenAlex

A collaborative initiative between school librarian, professional consultant, Principal, teachers and local community in a public high school aims to support students who are low-achieving or belong to low-income families in Japan by setting up a café inside the library. The importance of a cafe in school libraries are revealed from the three point of views; Ibasho, Youth support and Information literacy education. 1) Piccari Café is a value for students not only to add the choices of ibasyo in school but also to let them know about ibasyo outside school. 2) Piccari Café works as a platform for each three levels of prevention interventions: universal, selective, and indicated. 3) Piccari Café potentially provides students information literacy education without teaching. This case revealed that school library with a meeting, learning and creative function has the potential to generate a platform for students to strengthen links with school and society.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0090.002
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.023
GPT teacher head0.265
Teacher spread0.242 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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