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Record W3143303088 · doi:10.29173/iasl8030

Accelerated Reader and Information Policy, Information Literacy, and Knowledge Management: U.S. and International Implications

2021· article· en· W3143303088 on OpenAlexvenueno aff
Nancy Everhart, Eliza T. Dresang, Bowie Kotrla

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Information literacyLiteracyStyle (visual arts)Management stylesRelation (database)Information managementData collectionPublic relationsPolitical sciencePsychologySociologyKnowledge managementPedagogyComputer scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

Qualitative and quantitative analyses of the relationship between the Accelerated Reader (AR), a computerized reading management program, and information policy, information literacy, and knowledge management are drawn from data collected in the U.S., Scotland, and England. A study of 632 of the poorest U.S. schools shows a strong relationship between national information policy regarding achievement in the No Child Left Behind Act of 2001 and local decisions to use AR, expectations for literacy, and library collection development. Investigation in the U.K. schools finds that (a) motivational style interacts with gender in relation to the competitive and social aspects of the AR program, (b) the level of program implementation does not correlate with breadth of reading, and (c) management aspects of the program are not utilized effectively. Results suggest that how the AR program relates to information policy, information literacy, and knowledge management has importance for school librarians and libraries

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.005
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 head0.032
GPT teacher head0.341
Teacher spread0.310 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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