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Record W4254805250 · doi:10.11645/jil.v12i1.2322

Information literacy skills on the go

2018· article· en· W4254805250 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Information Literacy · 2018
Typearticle
Languageen
FieldComputer Science
TopicWeb and Library Services
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInformation literacyFluencyLifelong learningPsychologyLiteracyDigital literacyMedical education21st century skillsPedagogyMathematics educationMedicine

Abstract

fetched live from OpenAlex

Students’ understanding and integration of information literacy (IL) skills are fundamental to higher education and lifelong learning. Development and implementation of thirteen mobile lessons application (http://renmil.ca/ ) in the Mobile Information Literacy Tool (MIL) was the result of a unique collaboration between faculty and the library. Lessons demonstrated how to locate, evaluate, and use information effectively. Mixed methods pilot study findings (Hanbidge, Sanderson, & Tin, 2015) informed the Canadian project’s second stage analysis to determine fluency in digital literacy skills and testing of the MIL tool. One hundred and twenty-eight undergraduate Arts students from eight different classes majoring in psychology, social work, English or social development studies participated in the study to determine the effectiveness of using mobile technology to enhance their IL skills. Preliminary successes and experiences with overcoming the barriers to support anytime, anywhere student mobile information literacy training are discussed and future directions are recommended.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.060
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.004
GPT teacher head0.229
Teacher spread0.225 · 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