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Record W2995758552 · doi:10.1111/lit.12208

Making visible the literacy practices of elders through the<i>day in the life</i>methodology: considerations for literacy education across the lifespan

2019· article· en· W2995758552 on OpenAlexafffund
Rachel Heydon, Roz Stooke, Catherine Ann Cameron, Emma Cooper, Susan O’Neill

Bibliographic record

VenueLiteracy · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsSimon Fraser UniversityUniversity of British ColumbiaWestern University
FundersSocial Sciences and Humanities Research Council of CanadaWestern University
KeywordsLiteracyThrivingEveryday lifeCritical literacyLiteracy educationPedagogyPosthumanSociologyPsychologyCurriculumSocial scienceAestheticsPolitical science

Abstract

fetched live from OpenAlex

Abstract This pilot study uses ‘ day in the life ' methodology to observe the everyday literacy practices of a self‐identified thriving elder. Through the case of one nonagenarian female residing in an assisted living community in the United States, we identified the multimodal, posthuman nature of this elder's literacies, exploring how they were connected to a sense of well‐being and the types of literacies that remain relevant across the lifespan. We further consider what the insights gained from such a study might teach about literacy education more generally. We advocate for education that keeps open people's literacy options across the lifespan through acknowledging and cultivating the myriad interrelated constituents of literacies, including the physical, social and political.

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)0.023
metaresearch head score (Gemma)0.018
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 score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.007
Scholarly communication0.0050.004
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.164
GPT teacher head0.436
Teacher spread0.272 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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