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Record W4408799208 · doi:10.7202/1116926ar

Renouveler la recherche qualitative par les méthodes sensibles et les médias du co-design pendant la Covid-19

2024· article· fr· W4408799208 on OpenAlexvenueno aff
Marie-Julie Catoir-Brisson

Bibliographic record

VenueNouvelles perspectives en sciences sociales · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ChemistryBiologyMedicineVirology

Abstract

fetched live from OpenAlex

Cet article porte sur les dispositifs d’enquêtes mobilisés dans des recherches par le co-design dans des champs d’intervention diversifiés (santé, climat, risques naturels) en contexte de pandémie. Les méthodes sensibles et les médias du co-design jouent un rôle dans la méthodologie de recherche, pour s’immerger dans le terrain, co-construire les projets avec les participants et communiquer sur le projet en cours. L’accent est mis sur la phase d’enquête avec divers matériaux : photographie, vidéo, illustration, médias sociaux. La mobilisation de ces médias dans l’enquête soulève des questions méthodologiques, abordées à partir d’exemples : comment ces méthodes ont émergé dans des situations particulières et avec des participants spécifiques ? Quels médias ont été mobilisés et avec quelles visées ? L’article souligne les contributions et défis méthodologiques de ces approches renouvelées de la recherche qualitative. L’enjeu est aussi d’analyser les relations affectives générées par ces médias pour aborder des sujets complexes impliquant la sensorialité et d’en proposer un cadre réflexif pour la recherche qualitative.

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)0.163
metaresearch head score (Gemma)0.088
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.623
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1630.088
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0050.046
Scholarly communication0.0020.001
Open science0.0020.000
Research integrity0.0020.003
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 head0.919
GPT teacher head0.726
Teacher spread0.192 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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