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Record W3035991374 · doi:10.29173/slw8245

AASL National School Library Standards: Progress toward Implementation

2021· article· en· W3035991374 on OpenAlexvenueno aff
Elizabeth Burns

Bibliographic record

VenueSchool Libraries Worldwide · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsSchool libraryMedical educationProfessional developmentBest practiceLibrary sciencePolitical sciencePsychologyPedagogyMedicineComputer science

Abstract

fetched live from OpenAlex

This mixed-methods, exploratory case study examines perceptions of the implementation of the U.S. National School Library Standards for Learners, School Librarians, and School Libraries. A survey was first distributed to gain insight into school librarian implementation of the NSLS. Participants included practitioners in the state of Virginia in the United States who had attended professional development training on the National School Library Standards (NSLS). A sample was then interviewed about their implementation. Using the standards implementation levels identified by the California Common Core Implementation Task Force (2019) as a framework, findings indicate that after one year of training and use, many librarians remain at the awareness phase of implementation. Findings do suggest some areas in which practitioners have shown progress as they implement the standards into practice. Finally, common challenges or barriers to implementing the NSLS are noted. Future directions for research and recommendations for training at both the school/district level and national organization level are suggested.

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)0.059
metaresearch head score (Gemma)0.088
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.088
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.003
Scholarly communication0.0110.008
Open science0.0020.006
Research integrity0.0020.002
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 head0.020
GPT teacher head0.333
Teacher spread0.313 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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