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Record W4386346051 · doi:10.4324/9781003254829-39

Epistemology and methodology of participatory research with older adults

2023· preprint· en· W4386346051 on OpenAlexaffabout
Myriam Leleu, Mario Paris, Hugo Bertillot, Suzanne Garon, Robert Grabczan, Olivier Masson, Thibauld Moulaert, Damien Vanneste

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversité de SherbrookeUniversité de Moncton
Fundersnot available
KeywordsCitizen journalismParticipatory action researchSociologyPsychologyComputer scienceAnthropologyWorld Wide Web

Abstract

fetched live from OpenAlex

Promoted by the World Health Organization, Age-friendly Cities and Communities (AFCC) projects are multiplying, presenting variations related to national contexts and methodologies put in place to support the involvement of older adults. Participation is modulated according to local policies, environments, methods of participation, profiles of older adults, local actors, etc. Considering the aim of social inclusion, participatory process, empowerment, and collaborative partnership, many differences exist between AFCC projects. This chapter proposes a comparative dialogue on participatory methods of AFCC with an emphasis on their challenges, effects, strengths, and limits. In this perspective, four AFCC case studies crossing different French-speaking contexts of Europe and Canada will be presented and analyzed. The first one is about older adults’ participation within the management of cities (Quebec, Canada). The second concerns older adults of a rural territory, participatory practices, and inclusive process (France). The third case observes a French minority community and the challenges of mobilizing social actors on a community housing project for older adults (New Brunswick, Canada). The fourth analyzes the role of committed actors and the potential for social change through the empowerment of older adults (Wallonia, Belgium).

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)0.051
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.959
GPT teacher head0.749
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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Same topicParticipatory Visual Research MethodsFrench-language works237,207