Decolonial Community Based Research in Psychology:Case Studies for Students and Educators
Bibliographic record
Abstract
Decolonising involves centring the perspectives and knowledges of Indigenous and Global Majority communities. Participatory and community-based research methods can be decolonial when done properly, as they are grounded in building equitable partnerships with minoritised communities and valuing their lived experience and ways of knowing. Aligned with Indigenous principles, these approaches support the co-construction of knowledge that is reflective, contextual and rooted in relational accountability. This booklet presents five real examples of participatory and community-based research embedding decolonial reflections, primarily within health, psychology, and related fields. It is designed for students and educators and offers concrete, field-based examples grounded in researchers’ expertise and experiences. It focuses on strategies for designing and conducting research that is genuinely mutually beneficial and equitable. Case study 1 features Dr Nilu Ahmed’s exploration of the experiences and stories of Bangladeshi women living in the UK. Case study 2 highlights Dr Taylor-Jai McAlister’s experiences with suicide prevention research in and with Aboriginal communities in Australia. In case study 3, Catherine Jameson discusses Patient, Public and Community Involvement and Engagement (PCIE) in UK health research with ethnically diverse communities. Case study 4 presents Kacey Martin’s insights from a sexual health project with young Aboriginal people in Australia. Finally, case study 5 explores Dr Vera da Silva Sinha’s research on event-based time languages with Indigenous communities in the Brazilian Amazon. The booklet was organised and edited by Isabella Macedo de Lucas as part of her PhD project on decolonising research methods education in psychology. Her work is funded through a cotutelle PhD studentship between the University of Bristol and Macquarie University. Isabella’s supervisors are Associate Professor Peter Allen, Associate Professor Nilu Ahmed, and Professor Christopher Kent at the University of Bristol, as well as Professor Greg Downey and Dr Umut Ozguc at Macquarie University. The case studies were co-designed in collaboration with and co-edited by Dr Nilu Ahmed, Dr Taylor-Jai McAlister, Catherine Jameson, Kacey Martin, and Dr Vera da Silva Sinha. The artwork featured in this booklet is by Auá Mendes, an Indigenous artist from the Mura people of Brazil. Auá is a graphic artist, illustrator, muralist, and art educator born in Manaus (1999) and based in São Paulo since 2020. She holds a degree in graphic design, and her career combines her cultural identity with visual productions that engage with contemporary themes. Her work includes impactful urban interventions (murals up to 60 meters) and participation in projects and campaigns addressing issues such as female empowerment, diversity, and the appreciation of the Amazon. Auá Mendes is recognised as a significant voice in contemporary Brazilian art, and her work has been exhibited internationally. Connect with Auá on Instagram @aua___art.
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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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".