Community Development-Grounded and Led: A Methodological Approach for Ethical, Community-Driven Inquiry With Roma in Ireland
Bibliographic record
Abstract
This paper presents a community development-grounded and community-led research approach that explores Roma employment experiences in Ireland. The study used a research process rooted in the values of participation, collective analysis, and action for social justice, while centring Roma epistemologies and community accountability. The research was developed collaboratively by community workers and Roma researchers from Pavee Point Traveller and Roma Centre and Department of Applied Social Studies, Maynooth University. It used narrative, conversation-based interviews inspired by the Biographic Narrative Interpretive Method, which supported participant-led storytelling and oral traditions central to Roma culture. Ethics was guided by both university review and Pavee Point Traveller and Roma Centre’s internal Research Advisory Group. This paper explores five interrelated methodological dimensions: (1) grounding research in a community development process; (2) the composition and dynamics of a participatory research team; (3) Roma epistemologies; (4) community-led research governance, and (5) accountability to the community beyond the life of the project. The analysis and dissemination were co-produced, including co-authored and co-presented dissemination and public engagement. This paper adds to the growing field of community-driven research methods, demonstrating how research that is embedded in community development practice and guided by community development principles can move beyond extractive practices, contribute to epistemic justice, meaningful participation, and systemic change and by doing so can organically realise decolonising goals.
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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 | 0.120 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".