MétaCan
Menu
Back to cohort
Record W3150186548 · doi:10.29173/iasl7809

Core Interests of School Library Practitioner in Asia and Pacific Region

2021· article· en· W3150186548 on OpenAlexvenueno aff
Yumiko Kasai, Leslie Maniotes, Peng Han Lim, Susan La Marca

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsSuccessor cardinalLibrary scienceSchool libraryPolitical scienceAsia pacificChinaSociologyComputer science

Abstract

fetched live from OpenAlex

Internationally, there are well-known school library models including the U.S. model, with its strong groups of professionals, the British model, dependent on school library services in the community, and the Australian model, which can be described as either a successor to or a middle way between these two models. However, no independent school library model has been established in Asia. In Japan, the Library and Information Professions and Educations Renewal (LIPER) project was established in 2003 to study reforms to and the reorganization of library and information science education, with the members of the Japan Society of Library and Information Science. The School Library Initiatives for Asia & Pacific (SLAP) Forum, an international meeting for school library practitioners, was held in Tokyo in January 2013, and even before then an initiative was conducted as part of the studies spun off from the LIPER’s third stage. This paper reports on these topics.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.027
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0060.003
Scholarly communication0.0080.005
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0270.007

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 head0.082
GPT teacher head0.323
Teacher spread0.241 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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