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Record W2148779162 · doi:10.1115/1.802977.paper218

Information Literacy Training of Public Libraries: A Case from Canada

2009· book-chapter· en· W2148779162 on OpenAlexaboutno aff
Horng-Ji Lai

Bibliographic record

VenueASME Press eBooks · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsInformation literacyFocus groupMedical educationQuality (philosophy)LiteracyTraining (meteorology)Public relationsOrder (exchange)PsychologyPolitical sciencePedagogySociologyBusinessMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the current state of information literacy (IL) training and to identify the strategies and methods used by the Canadian public libraries in improving information literacy skills for their staff and patrons. Also, the study sought to identify problems associated with the development of information literacy training. This study employed document analysis, observations, and focus group interviews to collect research data. The focus group interview consisted of six library staff members. The research findings revealed that Canada's public libraries valued their roles as IL training providers and paid careful attention to staff development in order to provide efficient IL instruction for the public. Another issue explored in this study is that public libraries build partnerships with other organizations to extend their IL teaching responsibilities. Additionally, a major challenge, based on the research findings, is that public libraries need to let their staff understand the learning theories associated with IL education and adult learning in order to enhance the quality of this training.

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)0.006
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 score0.082
Threshold uncertainty score0.595

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0260.004
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.055
GPT teacher head0.259
Teacher spread0.204 · 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

Citations30
Published2009
Admission routes1
Has abstractyes

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