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Record W3149275196 · doi:10.29173/iasl7987

Building capacity and continuous improvement of school libraries: The Delaware experience

2021· article· en· W3149275196 on OpenAlexvenueno aff
Ross J. Todd

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumSchool libraryGovernorLiteracyCapacity buildingPolitical scienceClass (philosophy)Process (computing)Reading (process)Professional developmentAction researchState (computer science)Information literacyPedagogyPublic relationsMathematics educationSociologyEngineeringLibrary sciencePsychologyComputer science

Abstract

fetched live from OpenAlex

This research paper is in two parts. “Part I: The Evidence” documents the background, purpose, methodology and findings of the Delaware School Library Infrastructure Study undertaken on behalf of the Delaware Governor’s Task Force, and highlights some key issues and concerns that have formed the basis for . “Part 2: From Evidence to Action” documents the processes and professional actions involved in developing a sustainable program of improvement for school libraries in Delaware through engaging with the research evidence. This research and development process, initiated in 2005, is an ongoing evidence-based practice program engaging multiple partnerships at school district and state department of education to focus on continuous improvement and capacity building of school libraries in the state of Delaware. At its center is a process of engaging school librarians in a research-based, data-driven cycle of transforming school libraries so that they can play a central and identifiable role in curriculum implementation, student achievement, reading, and literacy development in Delaware’s schools, and to ensure that Delaware’s school libraries play a role in world class learning and literacy in the state.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.098
GPT teacher head0.365
Teacher spread0.267 · 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 designQualitative
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

Citations2
Published2021
Admission routes1
Has abstractyes

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