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Record W1501754058 · doi:10.29173/iasl8131

Implementing Flexible Scheduling in Elementary Libraries

2021· article· en· W1501754058 on OpenAlexvenueno aff
Joy McGregor

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumImplementationScheduling (production processes)Computer scienceFlexible schedulingScheduleMathematics educationPedagogyEngineeringOperations managementSoftware engineeringSociologyOperating systemPsychology

Abstract

fetched live from OpenAlex

Flexible scheduling in school libraries can provide a mechanism for achieving the learning goals and objectives of both the school library program and the curriculum by making information available at the point of need and by promoting use of the library and its services at a relevant moment instead of on a predetermined schedule. This study examined the successful implementation of flexible scheduling in six elementary schools where no funding was received to support implementation. Telephone interviews with librarians, principals, and teachers provided the data. The preliminary results indicate that, in those schools with successful implementations, flexible scheduling was only a tool to achieve another internal curricular objective, such as curriculum integration or literacy improvement. In addition, education of stakeholders is essential; and it must emphasize strategies for effective use of the programs supported by flexible scheduling and their potential learning outcomes rather than simply provide information on how flexible scheduling works.

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)0.022
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 score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.308
Teacher spread0.277 · 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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