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Record W6929935367 · doi:10.5281/zenodo.10005499

Layers of Meaning: Co-Designing with Bilingual Youth to Unearth their Reading Practices

2023· article· en· W6929935367 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Shared readingExtensive readingThrough-the-lens meteringNeuroscience of multilingualismData collectionLanguage acquisition

Abstract

fetched live from OpenAlex

This paper examines migration and belonging through the lens of heritage language and young people's engagement with reading for pleasure. We argue that a better understanding of multilingual youths' engagement with reading can help libraries devise better ways of supporting their intertwined identities as members of linguistic, cultural, and reading communities. This study presents the theoretical framework and research design for an ongoing study of Canadian youth in Ontario (aged between 13-18, and members of the French official language minority community) along with their engagement with reading (not limited to print texts and books alone). A co-design approach was selected to engage youth in exploring their relationship to reading, identity, and libraries. Data collection is currently underway and involves pre- and post-surveys of youth, workshop materials and fieldnotes, a reading diary activity, and interviews with school and public librarians. An interpretive lens will be essential to making sense of the rich data stemming from a range of artifacts collected and analyzed.

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)0.015
metaresearch head score (Gemma)0.014
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 score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.007
Scholarly communication0.0080.004
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.274
Teacher spread0.226 · 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
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicBacterial Genetics and BiotechnologyFrench-language works237,207