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Record W4383769529 · doi:10.56687/9781447335924-010

Between ageing and ageism: portrayals of online dating in later life in Canadian print media

2018· book-chapter· en· W4383769529 on OpenAlexaboutno aff
Julia Rozanova, Mineko Wada, Laura Hurd Clarke

Bibliographic record

VenuePolicy Press eBooks · 2018
Typebook-chapter
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsAgeingHistoryPsychologyMedicine

Abstract

fetched live from OpenAlex

This chapter examines how Canadian print media portrays online dating in later life, based on data from 144 articles published between 2009 and 2011. Results indicate that online dating extends romantic lives and catalyzes successful aging, while revealing that older persons are no longer competitive in the conventional romantic ‘market.’ Goffman’s concepts around the management of ‘spoiled identity’ and ‘stigma’ are particularly useful in this chapter’s portrayal of online dating as evidence that older adults’ success and failure at later life romance reflect wider debates about the nature of ‘normal’ aging versus idealized ‘successful’ aging on an everyday level. Thus, examples are provided of individuals whose behavior and attitudes successfully refuted and subverted the stereotypes about later life both as a de-sexualized zone and a negotiated one whereby aging-related stigma are brought to the ‘front-stage’ of public discourse while exposing the ‘back-stage’ processes and practices of growing older.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.052
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0160.008
Scholarly communication0.0110.004
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0150.001

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 head0.130
GPT teacher head0.392
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2018
Admission routes1
Has abstractno

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