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Record W2994379632 · doi:10.11645/13.2.2627

Exploring value as a dimension of professional information literacy

2019· article· en· W2994379632 on OpenAlexaff
Sara Sharun

Bibliographic record

VenueJournal of Information Literacy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsMount Royal University
Fundersnot available
KeywordsInformation literacyValue (mathematics)PhenomenographyContext (archaeology)SociologyPedagogyHealth literacyDimension (graph theory)Public relationsHealth careMedical educationPsychologyMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

This study presents a critical exploration of one of the ACRL Framework concepts by examining it in the context of professional practice. Semi-structured interviews were conducted with health and human service professionals at a community health centre to explore how information literacy (IL) is experienced in the workplace. Value emerged as the dominant theme in participants’ descriptions of their information practices. This concept was conceived of predominantly in the context of personal and professional relationships that existed within the systems and structures of the physical workplace, professional practice and the health and social care system. Using phenomenography as a methodological approach, this study presents a lens through which to see the nature and significance of information value in various contexts beyond academia, and invites librarians to consider how evidence from workplace and professional settings may inform IL instruction to students, especially those entering health and human service professions.

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)0.012
metaresearch head score (Gemma)0.025
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0050.034
Scholarly communication0.0130.012
Open science0.0010.009
Research integrity0.0020.004
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 head0.021
GPT teacher head0.305
Teacher spread0.284 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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