Hierarchy and inequality in research: Navigating the challenges of research in Ghana
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.052 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".