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Record W264958524 · doi:10.29173/slw6958

Student Learning Through Ohio School Libraries, Part 1: How Effective School Libraries Help Students

2001· article· en· W264958524 on OpenAlexvenueno aff
Ross J. Todd, Carol Collier Kuhlthau

Bibliographic record

VenueSchool Libraries Worldwide · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsSchool libraryMathematics educationAgency (philosophy)PsychologyPedagogySociologyComputer scienceLibrary science

Abstract

fetched live from OpenAlex

This article provides an overview of the Student Learning Through Ohio School Libraries research study undertaken from October 2002 through December 2003. The study involved 39 effective school libraries across Ohio; the participants included 13,123 students in grades 3 to 12 and 879 faculty. The focus question of the study was: How do school libraries help students with their learning in and away from school? The findings, both quantitative and qualitative, showed that effective school libraries help students with their learning in many ways across the various grade levels. Effective school libraries play an active rather than passive role in students' learning. The concept of help was understood in two ways: helps-as-inputs, or help that engages students in the process of effective learning through the school library; and helps-as-outcomes/impacts, or demonstrated outcomes of meaningful learning-academic achievement and personal agency. The study shows that an effective school library is not just informational, but transformational and formational, leading to knowledge creation, knowledge production, knowledge dissemination, and knowledge use, as well as the development of information values.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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 designObservational
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

Citations119
Published2001
Admission routes1
Has abstractyes

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