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The Polytechnic Predicament: An Exploratory Study in Tutor Perceptions of Information Literacy

2022· article· en· W4313293154 on OpenAlexaffvenue
D. A. Buchanan, Deirdre Grace, Amanda Grey, Jeffery Verbeem

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsKwantlen Polytechnic UniversityBritish Columbia Institute of Technology
Fundersnot available
KeywordsInformation literacyRelevance (law)TUTORFocus groupPerspective (graphical)Exploratory researchQualitative researchPsychologyPedagogyMathematics educationSociologyComputer sciencePolitical scienceSocial science

Abstract

fetched live from OpenAlex

The purpose of this exploratory case study is to consider from peer tutors’ perspective the relevance of information literacy (IL) in their roles as tutors, students and in their everyday lives. The research used a qualitative methodology, wherein nine participants shared thoughts and reflections in course discussion forums in response to six online modules, each outlining one the the six frames of the ACRL information literacy framework. The data-gathering phase of the study was bookended by focus groups that were also recorded. Analysis of these various discussions reveals that while tutors see the relevance of IL in their everyday lives, their responses in terms of their roles as tutors and students varies depending on the nature of their program. The need to budget research time efficiently in response to a heavy course load prevents some from pursuing information more broadly or deeply than strictly necessary. The paper considers implications of these insights for further inquiry into the library’s role in advancing IL development in a polytechnical environment.

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)0.010
metaresearch head score (Gemma)0.030
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 score0.011
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0110.009
Scholarly communication0.0080.005
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.050
GPT teacher head0.376
Teacher spread0.325 · 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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Same venuePartnership The Canadian Journal of Library and Information Practice and ResearchSame topicLibrary Science and Information LiteracyFrench-language works237,207