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Record W7073948865

Epistemology and Methodology of Participatory Research with Older Adults: A Comparison of Four Age-friendly City National Experiences

2023· book-chapter· en· W7073948865 on OpenAlexaboutno aff

Bibliographic record

VenueDIAL (Catholic University of Leuven) · 2023
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsReflexivityParticipatory action researchCitizen journalismEmpowermentAction (physics)Action researchCollective action
DOInot available

Abstract

fetched live from OpenAlex

The aim of this chapter is to present a comparative approach on participatory methods within four AFCC case studies from different French-speaking contexts.1 First, the Université de Sherbrooke in Quebec (Canada) is interested in observing older adults’ participation within the management of cities and the older adults’ organizations involved in AFCC. Second, the Université catholique de Lille (France) leads a research that consists in supporting older adults of a rural territory to analyze by themselves the different dimensions of their own participatory practices, in order to reinforce collective reflexivity and inclusive process. Third, at the Université de Moncton, in New Brunswick (Canada), participatory research seeks to help a French minority community implement an AFCC’s action on community housing for older adults and, in doing so, highlights the challenges of rallying social actors and older adults in a knowledge mobilization process. Fourth, the Université catholique de Louvain in Belgium, through the Age-friendly Wallonia project, challenges the development of a collective conscience and the potential for social change through the empowerment of older adults. Then, a researcher from the Université Grenoble Alpes (France) examines the relationships between these AFCC experiences to highlight common challenges and opportunities through such a rare international comparison.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.195
GPT teacher head0.347
Teacher spread0.152 · 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 designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

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