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Record W2108640316 · doi:10.1002/meet.14504701336

Evidence‐based information literacy instruction: Curriculum planning from the ground up

2010· article· en· W2108640316 on OpenAlexaff
Lisa M. Given, Heidi Julien, Dana Ouellette, Jorden Smith

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInformation literacyPreparednessCurriculumMedical educationAuditPsychologyMathematics educationPedagogyPolitical scienceMedicineBusiness

Abstract

fetched live from OpenAlex

Abstract The purpose of this longitudinal research study is to assess the information literacy (IL) skills of grade 12 students as they transition to university in order to determine their preparedness for academic work in the digital age. This poster reports the results of the first phase of this study which included a university‐wide information literacy instruction (ILI) audit, as well as the administration of the quantitative Information Literacy Test (ILT) to 103 grade 12 students. Results indicate a gap between the expectations and skills required in secondary and post‐secondary education. The results of this study contribute new knowledge to the research literature on IL, by providing a unique understanding of the information literacy skills possessed by grade 12 students as they transition to university. This will also be important for professional practice by providing librarians tasked with ILI with evidence enabling construction of tailored curriculum to address specific IL deficits shown by new students.

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)0.015
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.305
Teacher spread0.285 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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