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Record W3143521555 · doi:10.29173/iasl8157

Is the West best?: Education for School Information Service Professionals

2021· article· en· W3143521555 on OpenAlexvenueno aff
Jo Hallein

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Service (business)Developing countryProfessional developmentTraining (meteorology)Best practicePublic relationsDeveloped countryPolitical scienceBusinessMedical educationEconomic growthSociologyMarketingGeographyEngineeringMedicineEconomics

Abstract

fetched live from OpenAlex

School Library Educators in developing countries have, in the main, been training School Infomration Professionals using models of education developed in the United States and Europe. While there have been some local modifications to these western models few countries have developed truly local education programs for Infomation Servie Professional. This is due, in part, to the fact that the Information Services that students are being educated to work with are global information services often based on series developed in the United States and Europe. Many of the advisers who helped to set up school library training programs are from Western Countries and a large proportion of the staff lecturing in these programs have been educated in Western Countries. The paper will conclude by discussing if western models of education for School Information Service Professionals are the most appropriate models given the increased globilization of information services or whether locally developed models can best service the needs of those training to work in developing countries.

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)0.006
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0100.010
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0110.002

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 head0.031
GPT teacher head0.335
Teacher spread0.304 · 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
Published2021
Admission routes1
Has abstractyes

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