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Record W3197530674 · doi:10.3389/fpubh.2021.662231

The “Vulnerability” Discourse in Times of Covid-19: Between Abandonment and Protection of Canadian Francophone Older Adults

2021· article· en· W3197530674 on OpenAlexaffabout
Martine Lagacé, Amélie Doucet, Pascale Dangoisse, Caroline D. Bergeron

Bibliographic record

VenueFrontiers in Public Health · 2021
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsPublic Health Agency of CanadaUniversité du Québec à MontréalUniversity of Ottawa
Fundersnot available
KeywordsPandemicAbandonment (legal)Context (archaeology)Stereotype (UML)Vulnerability (computing)FrenchSociologyPsychologyCoronavirus disease 2019 (COVID-19)Discourse analysisGender studiesSocial psychologyPolitical scienceMedicineHistoryLinguisticsDisease

Abstract

fetched live from OpenAlex

The Covid-19 pandemic has been particularly difficult for older Canadians who have experienced age discrimination. As the media can provide a powerful channel for conveying stereotypes, the current study aimed to explore how Canadian Francophone older adults and the aging process were depicted by the media during the first wave of the Covid-19 pandemic, and to examine if and how the media discourse contributed to ageist attitudes and behaviors. A content analysis of two French Canadian media op-eds and comment pieces ( n = 85) published over the course of the first wave of the pandemic was conducted. Findings reveal that the aging process was mainly associated with words of decline, loss, and vulnerability. More so, older people were quasi-absent if not silent in the media discourse. Older adults were positioned as people to fight for and not as people to fight along with in the face of the pandemic. The findings from this study enhance the understanding of theories and concepts of the Theory of Social Representations and the Stereotype Content Model while outlining the importance of providing older people with a voice and a place in the shaping of public discourse around aging. Results also illustrate the transversality and influence of ageism in this linguistic minority context.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.366
Teacher spread0.319 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations28
Published2021
Admission routes2
Has abstractyes

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