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Record W2107433598

Connecting British Columbia (Canada) school libraries and student achievement: A comparison of higher and lower performing schools with similar overall funding

2011· article· en· W2107433598 on OpenAlexaboutno aff
Ken Haycock

Bibliographic record

VenueSan José State University ScholarWorks (San Jose State University) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsStudent achievementSchool libraryGovernment (linguistics)Academic achievementStandardized testTest (biology)Achievement testMathematics educationPolitical scienceMedical educationSchool districtPsychologyLibrary scienceMedicineComputer science
DOInot available

Abstract

fetched live from OpenAlex

Research over time has established associations between components of the school library and student achievement. This study was designed to investigate these associations in schools in British Columbia (Canada) where the government provides equitable funding of public schools while allowing individual school districts and schools to determine individual funding priorities. Findings replicated what numerous previous studies have shown: higher student standardized test scores were associated with a school library that is more accessible, better funded, professionally staffed, managed, stocked, integrated and used. Findings moreover pointed to higher student achievement in those schools where greater resources, from the same limited allocation were assigned to school libraries. Results of this study are of practical interest to policy makers, school and library administrators, and educators with a vested interest in student achievement and the future of school libraries. Reprinted by permission of the publisher.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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

Citations18
Published2011
Admission routes1
Has abstractyes

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