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Record W3176075384 · doi:10.1177/00914150211024173

Media Portrayal of Older Adults Across Five Canadian Disasters

2021· article· en· W3176075384 on OpenAlexafffundabout
Samantha A. Oostlander, Olivier Champagne-Poirier, Tracey O’Sullivan

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversité de SherbrookeUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContext (archaeology)StructuringMedia coverageConstructivist grounded theoryOlder peopleValue (mathematics)SociologyPsychologyGrounded theoryGender studiesPolitical scienceMedia studiesGerontologyHistoryMedicineSocial scienceQualitative researchComputer science

Abstract

fetched live from OpenAlex

We conducted a constructivist grounded theory approach in which discourse analysis was used to explore how Canadian news media portrays older adults and aging in a disaster context. We analyzed 119 articles covering five Canadian disasters and identified four themes: (a) stereotypes of older adults are presented on a positive-negative continuum in journalistic coverage of disasters, (b) journalistic coverage tends to exclude perspectives of older adults from relevant discourse, (c) journalists assess the value of losses for older adults-"home" as a central concept, and (d) disasters are framed as disrupting retirement ideals. A model was created to provide an overview of the journalistic coverage of older adults in disaster contexts. Understanding how old age and aging are presented by the media in a disaster context is important because it has further implications for informing and structuring disaster risk reduction policies.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.096
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0140.013
Science and technology studies0.0110.004
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.314
Teacher spread0.296 · 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

Citations17
Published2021
Admission routes3
Has abstractyes

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