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Record W4394948497 · doi:10.29173/ias/7937

From Learning to Read to Reading to Learn: School Libraries, Literacy and Guided Inquiry

2021· article· en· W4394948497 on OpenAlexvenueno aff
Ross J. Todd

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Inquiry-based learningLiteracyMathematics educationComputer scienceLearning to readInformation literacySchool libraryPsychologyPedagogyWorld Wide WebLinguistics

Abstract

fetched live from OpenAlex

School libraries are about the future. They are about the development of knowledgeable and knowing young people; young people who have the ability to read the word and the world, and who can live their lives as thinking, informed, knowledgeable and productive citizens of an increasingly inter-connected world. They are about young people who have the knowledge, skills, attitudes and values to invest wisely in confidently shaping their own futures and their lives as family, community and workplace members. Reading, Knowing and Doing are the multiple faces of the future global citizens that we nurture in our schools. Reading, Knowing, Doing, as the multiple faces of literacy, are the multiple faces of quality school libraries. And Reading, Knowing and Doing are at the heart of informed, in-tune, and in-touch school librarians committed to providing the best opportunities for our students to learn to use their minds well.

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)0.003
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0050.021
Scholarly communication0.0200.018
Open science0.0010.011
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.055
GPT teacher head0.346
Teacher spread0.291 · 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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