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Record W2611861339 · doi:10.29173/cais919

Numeracy Programming for Children in Canadian Public Libraries

2016· article· fr· W2611861339 on OpenAlexvenueaboutno aff
Samantha West, Michael B McNally, Dinesh Rathi

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsNumeracyLiteracyRepertoireLibrary scienceChild careHumanitiesSociologyPolitical sciencePsychologyPedagogyComputer scienceMedicineArtPediatrics

Abstract

fetched live from OpenAlex

The proposed study was conducted to analyze thevariety of programs offered by public libraries inCanada for children to develop their literacy skills,particularly numeracy literacy skills. The findingsincluded in the paper are based on the thematicanalysis of information included on individuallibraries’ websites. Key findings suggest that althoughCanadian public libraries have a few numeracy skillsprograms in their repertoire, there is a need forgreater programming to develop these skills.L’étude proposée a été menée pour analyser lavariété de programmes offerts par les bibliothèquespubliques au Canada, destinés au développementdes compétences, en particulier en littératie et ennumératie chez les enfants. Les conclusions figurantdans l’étude sont basées sur l’analyse thématiquedes informations présentes sur les sites Webindividuels des bibliothèques. Les principalesconclusions suggèrent que si les bibliothèquespubliques canadiennes ont bien quelquesprogrammes de compétences en numératie dans leurrépertoire, il y a un besoin de programmationsupplémentaire pour développer ces compétences.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.070
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.025
GPT teacher head0.256
Teacher spread0.231 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSISame topicLibrary Science and Information LiteracyFrench-language works237,207