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Record W1940026565 · doi:10.20360/g27w2c

The Value of Writing for Senior-Citizen Writers

2015· article· en· W1940026565 on OpenAlexaffvenue
Jeff Park, Beverley Brenna

Bibliographic record

VenueLanguage and Literacy · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLiteracyIdentity (music)Value (mathematics)Professional writingAcademic writingFunction (biology)Life writingCreative writingWriting processPsychologySociologyPopulationPedagogyVisual artsLiteratureAestheticsNarrativeArtComputer science

Abstract

fetched live from OpenAlex

This qualitative case study explores writing and writing motivations of senior citizens age 65-93 who had entered a public library Writing Challenge. The research questions focused on how and why writing was important to this group as well as what patterns and themes emerged in their work. Data from questionnaires offered that the social aspect of writing appeared to be the strongest motivating factor for participation. Numerous individual reasons for writing were listed, and these, as well as the unique ideas presented in excerpts from the work itself, created a resonant picture of writing in participants’ lives. The resulting anthology contained a predominance of non-fiction, including life writing components within fictive pieces, utilizing the expressive function. Key themes included identity, olden days, progress, humour, nature, religion, and the love of family. Implications involve the importance of community writing events for writers who may not have other means of developing individual writing networks. Further research is recommended related to seniors and literacy to add to what is currently a limited academic viewpoint regarding this population.

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)0.010
metaresearch head score (Gemma)0.025
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 score0.020
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0200.010
Scholarly communication0.0140.007
Open science0.0020.010
Research integrity0.0020.002
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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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

Citations19
Published2015
Admission routes2
Has abstractyes

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