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Record W1945591307 · doi:10.18352/lq.9868

Developing data literacy competencies to enhance faculty collaborations

2015· article· en· W1945591307 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLIBER Quarterly The Journal of the Association of European Research Libraries · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInformation literacyComputer scienceLiteracyMathematics educationMedical educationWorld Wide WebPsychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

In order to align information literacy instruction with changing faculty and student needs, librarians must expand their skills and competencies beyond traditional information sources. In the sciences, this increasingly means integrating the data resources used by researchers into instruction for undergraduate students. Open access data repositories allow students to work with more primary data than ever before, but only if they know how and where to look. This paper will describe the development of two information literacy workshops designed to scaffold student learning in the biological sciences across two second-year courses, detailing the long-term collaboration between a librarian and an instructor that now serves over 500 students per semester. In each workshop, students are guided through the discovery and analysis of life sciences data from multiple sites, encouraged to integrate text and data sources, and supported in completing research assignments.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.103
GPT teacher head0.375
Teacher spread0.272 · 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