Structural and ethical challenges in participatory research with migrant and minority groups
Bibliographic record
Abstract
Abstract Based on experiences with participatory research projects with forced migrant and ethnic minority groups in Germany, Israel and Canada, Dr. Gottlieb will first reflect on the structural challenges that especially young researchers face when doing - or intending to do - participatory research. Secondly, she will discuss ethical issues that can arise in participatory research, in particular when grave inequalities, fragmentations and conflicts within the researched communities exist. In such contexts, certain generally valid research ethical questions merit particular attention; e.g. the questions ‘Who represents whom?’, ‘Who gets access to the research process and its benefits?’ and ‘How are direct and indirect benefits distributed among different community members?’ Another set of questions concerns potential discrepancies between common goals in participatory research - such as empowerment, agency, leadership and innovation - and community norms. E.g., what is the risk of participatory research projects intensifying existing internal, e.g. intergenerational or gender-based, rifts and/or getting their practice partners into conflict with their communities and customs? To what extent ought research encourage individuals within a community to “go against the stream”? In light of (post-)colonial histories and trauma such questions can be especially charged, both politically and emotionally. It is therefore a huge responsibility for the researcher to carefully consider the role of the study within its wider context, to weigh its potential (intended and unintended) effects and broader outcomes on individual and community levels, and to balance them with the study goals and intended benefits and its consequences for health research.
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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 | 0.460 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.035 | 0.094 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".