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Record W3142401568 · doi:10.29173/iasl7805

"Learning to be a Learner"

2021· article· en· W3142401568 on OpenAlexvenueno aff
Kasey Garrison, Robin Spruce

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningTheme (computing)MetacognitionPsychologyPedagogySocial skillsInterpersonal communicationMathematics educationCognitionComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

“Learning to be a learner” is how Tessa, a grades 6-12 teacher librarian, said she would describe the idea of self-regulated learning to her students. Teacher librarians are in a unique position within schools to truly serve as lifelong learning coaches for students, focused on process and skills instead of content. Further, their reach extends across levels in the school, touching every single student through their teaching and the development and maintenance of the library collection. This paper presents findings from a study investigating how US teacher librarians apply metacognitive strategies in their teaching. An unexpected theme emerged from the interviews as participants described lifelong learning skills they strive to impart to students. These skills included cognitive, personal, and interpersonal skills as defined by UNICEF (2003) and exemplified by the theme for the 2013 International Association of School Librarians’ Conference.

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)0.006
metaresearch head score (Gemma)0.011
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: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.014
Scholarly communication0.0060.008
Open science0.0010.009
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.003

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 head0.032
GPT teacher head0.300
Teacher spread0.269 · 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
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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