MétaCan
Menu
Back to cohort
Record W2979495841 · doi:10.29173/iasl7150

How Junior High School Students Spend Time in a Japanese School Library during Their Lunch Breaks: A Focus on the Role of Bookshelves

2017· article· en· W2979495841 on OpenAlexvenueno aff
Chizuko Arai

Bibliographic record

VenueIASL Annual Conference Proceedings · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyData collectionFocus (optics)Participant observationSociologyMedical educationMathematics educationPsychologyMedicineSocial scienceAnthropology

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate, through fieldwork, a school library as a gathering place for Japanese middle school students. We predicted that students would browse alone, silently. The research used cases studies based on a micro-ethnography method taken from observation data. Data were analyzed from three points of view, informal, public, and gathering, which was referred to as “the third place.” Oldenburg (1989), on which the study was based, focused on the role of bookshelves. We determine that bookshelves provide students with the public place open to all. Second, we suggest that bookshelves serve students as an informal place to spend time alone. Finally, while some students enjoy conversations with friends while among the shelves, others avoid interaction by using the shelves as barriers. Therefore, the bookshelves are not always a gathering place for a students.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.273
Teacher spread0.251 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIASL Annual Conference ProceedingsSame topicLibrary Science and AdministrationFrench-language works237,207