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Record W3142632133 · doi:10.29173/iasl8031

Using Large-Scale Assessments to Evaluate the Effectiveness of School Library Programs in California

2021· article· en· W3142632133 on OpenAlexvenueno aff
William W. Tarr, Stacy L. Sinclair-Tarr

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingDemographicsSchool libraryMathematics educationScale (ratio)Student achievementAcademic achievementPsychologyNorm (philosophy)Positive relationshipDemographyGeographyMedicineSocial psychologyComputer scienceLibrary sciencePolitical scienceCartographySociology

Abstract

fetched live from OpenAlex

This California study examined the relationship between the presence of school libraries, as defined by credentialed staffing, and student achievement, as measured by both criterionreferenced and norm-referenced assessments in both English-language arts and mathematics. Using the California School Characteristics Index to compare 4,022 schools with similar demographics at Grades 4, 7, and 10, both positive and negative statistically significant relationships were found between the presence of a school library and student achievement at Grades 4 and 7. There were no statistically significant positive relationships found at Grade 10. These findings do not support previous studies that used different methods of comparing schools with similar demographics. Also unlike previous studies, the overall effect sizes of the positive relationships were small, the average being an increase in student achievement of 2%. Factors within the school library at Grades 4 and 7 were also examined, and both positive and negative statistically significant relationships to student achievement were found.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.375
Teacher spread0.285 · 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 teacher head, 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

Citations0
Published2021
Admission routes1
Has abstractyes

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