MétaCan
Menu
Back to cohort
Record W4280498888 · doi:10.1177/14687941221098927

Hierarchy and inequality in research: Navigating the challenges of research in Ghana

2022· article· en· W4280498888 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueQualitative Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInequalityStigma (botany)Mental healthData collectionHierarchyPsychologyFocus groupSociologyPublic relationsApplied psychologyPolitical scienceSocial sciencePsychiatry

Abstract

fetched live from OpenAlex

This paper provides insights from experiences in data gathering and recruitment from two research projects on disability/mental health in Ghana. The focus of the study explores stigma amongst individuals diagnosed with mental illness and their caregivers. The study investigates the positioning of the researcher in a superior light by participants which often wrests power from those who should be considered the true experts of their own circumstances. Inequality in the interview process thus carried the risk of impacting the quality of the data, as some participants did not consider themselves as 'experts' of their condition. The paper explores strategies for addressing these challenges of hierarchy and inequality in the research process in the Global South. Based on the study, we report on our experiences as follows: (1) ensuring that participants are empowered to engage with researchers; and (2) training local researchers to engage in culturally sensitive research processes.

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.

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)0.668
metaresearch head score (Gemma)0.070
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6680.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.011
Science and technology studies0.0040.017
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.014
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 head0.897
GPT teacher head0.758
Teacher spread0.139 · 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