MétaCan
Menu
Back to cohort
Record W3133505226 · doi:10.29173/iasl7462

Mind the gap: School Librarians’ Anticipated and Preferred Professional Development Methods for Integration of Digital Textbooks

2021· article· en· W3133505226 on OpenAlexvenueno aff
Ji Hei Kang

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsMandateProfessional developmentState (computer science)Public relationsSociologyPedagogyPsychologyPolitical scienceMedical educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

While the mandate to adopt digital textbooks in the U.S. state of Florida is looming for the next school year, there has been few discussion about how to provide professional development for school librarians who will be expected to play a leadership role. This study identifies both preferred and anticipated methods of professional development of Florida school librarians and the gap between them. It has been found that school librarians wish to initiate their training with other colleagues through workshops and study groups, which will enable them to discuss digital textbooks, and build networks with respect to the new technology. The anticipated way, however, reflects the environment of solo librarians. In conclusion, this study urges that professional development that will enlarge their viewpoint and enhance their awareness of the need for methods of implementation of digital textbooks, is urgently required.

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)0.037
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.078
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0070.003
Scholarly communication0.0100.007
Open science0.0010.006
Research integrity0.0020.002
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 head0.094
GPT teacher head0.386
Teacher spread0.292 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIASL Annual Conference ProceedingsSame topicLibrary Science and Information LiteracyFrench-language works237,207