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Record W4378831244 · doi:10.1177/14687941231176947

Charlas y Comidas: Humanising focus groups and interviews

2023· article· en· W4378831244 on OpenAlexfundno aff
Yecid Ortega

Bibliographic record

VenueQualitative Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaInternational Research Foundation for English Language Education
KeywordsEthnographyFocus groupFeelingSociologyQualitative researchSpace (punctuation)PsychologyPedagogySocial psychologySocial scienceAnthropology

Abstract

fetched live from OpenAlex

Qualitative research has utilised focus groups and interviews to gather information from participants while conducting ethnographic research. This article explores the potential of alternative forms of collecting data that are more in line with the participants’ feelings, emotions and expectations. Charlas (chats) and Comidas (meals) were utilised in an ethnographic study with English teachers and their students in marginalised high schools in Bogotá, Colombia. I found that opening a safe space for participants to share their ideas, suggestions and comments while chatting informally or having a meal encourages leadership of the research process. This generated a sentiment of trust and bond which strengthen their sense of belonging to their academic community. This article contributes to the literature on alternative, critical and decolonial forms of doing research by considering ways to implement methods that acknowledge the cultural and linguistic backgrounds of the participants which strengthen humanising relationships, especially in marginalised contexts.

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)0.037
metaresearch head score (Gemma)0.022
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: Methods · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.009
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.724
GPT teacher head0.739
Teacher spread0.015 · 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
GenreMethods

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

Citations11
Published2023
Admission routes1
Has abstractyes

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