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Record W4281262112 · doi:10.5206/elip.v5i1.14542

Online Content Analysis of Ontario Public Libraries’ Sensory Programming and Service Offerings

2022· article· en· W4281262112 on OpenAlexvenueaboutno aff
Bridget Moynihan, Sara Clarke

Bibliographic record

VenueEmerging Library & Information Perspectives · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsAttendanceSensory systemService (business)Computer scienceMarketingPublic relationsBusinessPsychologyPolitical scienceCognitive psychology

Abstract

fetched live from OpenAlex

Sensory storytimes and other similar sensory programming and services, are those which have been specifically designed to respect the needs of autistic children and/or children with sensory processing disorders, even while also being open to a range of neurodiverse attendance. These programs and services are important ways in which public libraries can work to become more inclusive spaces. Nonetheless, sensory storytimes and other sensory programs and services are not as widely offered at Canadian libraries as they could be. In order to concretize and draw attention to this gap, this paper describes our content analysis research, conducted in July 2021, of Ontario Public Library (OPL) websites and their sensory programming and services listings. Although we found that some OPLs are offering sensory storytimes, as well as other sensory programming and services, we emphasize that offering and advertising these programs and services effectively remains an area of growth potential within Ontario.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.022
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.000
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 head0.063
GPT teacher head0.271
Teacher spread0.208 · 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 designObservational
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

Citations4
Published2022
Admission routes2
Has abstractyes

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