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Record W1582617615 · doi:10.1108/rsr-11-2014-0052

Situating information literacy in the disciplines

2015· article· en· W1582617615 on OpenAlexaff
Robert Farrell, William Badke

Bibliographic record

VenueReference Services Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsTrinity Western UniversityWestern University
Fundersnot available
KeywordsInformation literacyDisciplineOriginalitySociologyValue (mathematics)Embodied cognitionContext (archaeology)LiteracyCritical literacyEngineering ethicsKnowledge managementPublic relationsPedagogyComputer sciencePolitical scienceSocial scienceEngineeringQualitative research

Abstract

fetched live from OpenAlex

Purpose – The purpose of this article is to consider the current barriers to situating in the disciplines and to offer a possible strategy for so doing. Design/methodology/approach – The paper reviews current challenges facing librarians who seek to situate information literacy in the disciplines and offers and practical model for those wishing to do so. Phenomenographic evidence from disciplinary faculty focus groups is presented in the context of the model put forward. Findings – Disciplinary faculty do not have generic conceptions of information literacy but rather understand information-related behaviors as part of embodied disciplinary practice. Practical implications – Librarians dissatisfied with traditional forms of generic information literacy instruction marketing will find a method by which to place ownership on information literacy in the hands of disciplinary faculty. Originality/value – The article offers a unique analysis of the challenges facing current information literacy specialists and a new approach for integrating information literacy in the disciplines.

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)0.027
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.008
Science and technology studies0.0060.018
Scholarly communication0.0140.016
Open science0.0020.012
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.060
GPT teacher head0.385
Teacher spread0.326 · 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 designTheoretical or conceptual
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

Citations59
Published2015
Admission routes1
Has abstractyes

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