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Record W2980068812 · doi:10.1177/0340035219874046

Adult learning theories and autoethnography: Informing the practice of information literacy

2019· article· en· W2980068812 on OpenAlexaff
Karen Bordonaro

Bibliographic record

VenueIFLA Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsBrock University
Fundersnot available
KeywordsAutoethnographyReflexivityLifelong learningInformation literacyLiteracyPedagogyReflection (computer programming)SociologyInformal learningAdult educationPsychologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

The learning theories of self-directed learning and lifelong learning can inform the practice of information literacy in higher education for adult learners. These theories lend themselves to the use of autoethnography, a research methodology that relies on the exploration of lived experiences through reflexivity informed by theory. In conducting an autoethnography on information literacy, its practice appears as both a singular and a collective activity. Multiple ramifications for practice come from this exploration. These ramifications include considerations of choices, barriers, conducive learning environments, informal learning opportunities, and the need for reflection for adult learners. Applying the learning theories of self-directed learning and lifelong learning to the practice of information literacy offers librarians new and useful perspectives on its practice with adult learners.

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)0.029
metaresearch head score (Gemma)0.033
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 score0.029
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0040.018
Scholarly communication0.0070.012
Open science0.0010.006
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 head0.004
GPT teacher head0.277
Teacher spread0.273 · 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".

Quick stats

Citations8
Published2019
Admission routes1
Has abstractyes

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