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Record W3140200821 · doi:10.29173/iasl7784

School Library Advocacy Literature in the United States

2021· article· en· W3140200821 on OpenAlexvenueno aff
Ann Dutton Ewbank, Ja Youn Kwon

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsWarrantContext (archaeology)Position (finance)Political sciencePublic relationsWork (physics)Public administrationSociologyBusinessEngineering

Abstract

fetched live from OpenAlex

Despite evidence of school librarians’ impact on student achievement and multiple advocacy efforts, position eliminations and budget reductions continue across the United States. The researchers conducted a preliminary conceptual content analysis of the scholarly and practitioner literature about school library advocacy in the United States from 2001-2011 to determine methods of and rationales for advocacy. The most frequent advocacy method was distributing literature or information about school libraries. The most frequent reason cited in the literature for engaging in advocacy activities was in response to a potential funding or position reduction or elimination. Advocacy is highly contextualized and different situations may warrant different approaches. Viewing school library advocacy through an organizational evolution framework may frame the context. School library researchers should address the dearth of empirical and theoretical work on both the practice and impact of advocacy on the profession.

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)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0100.014
Science and technology studies0.0080.005
Scholarly communication0.0100.006
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.025
GPT teacher head0.290
Teacher spread0.265 · 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 designNot applicable
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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