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Record W2519199805 · doi:10.29173/iasl7537

Adapting Education for School Librarianship

2021· article· en· W2519199805 on OpenAlexvenueno aff
James Henri, Sandra Lee, Sue Trinidad, Alvin C. M. Kwan, Ming Lai

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorScheduleMedical educationExploratory researchHigher educationPsychologyPedagogyAction (physics)Teacher educationSociologyPolitical scienceMedicineManagement

Abstract

fetched live from OpenAlex

Over the past few years repeated calls have been made by teacher librarian educators for evidence based practice by teacher librarians. This study is an attempt to provide evidence for the adoption of innovative practice in a post-service, part time Bachelor of Education program. Part time tertiary students undertaking studies in education at the University of Hong Kong are often heard to voice the opinion that the demands of university study are excessive. While it is generally accepted that the Hong Kong lifestyle is hectic, that teachers have a heavy schedule, and that travel to and from the university campus is time-consuming, little useful data exists to allow university professors to better understand the plight of the students or to provide evidence from which action could be taken to better tailor courses to the needs of students. Likewise many assumptions are made about tertiary student motivation but these assumptions are probably not grounded in any research findings. This exploratory study was undertaken to determine the factors affecting the full-time teacher’s progress in their tertiary part-time study in school librarianship. The findings will better enable instructors to tailor teaching and learning to meet the needs of the part-time participant. Findings will also be informative for other part-time undergraduate programs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.022
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.314
Teacher spread0.274 · 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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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