Exploring practical and ethical dilemmas when conducting research with small population groups in First Nations communities: Privileging stories as data, and data as stories
Bibliographic record
Abstract
Purpose When working with small population groups, answering consequential research questions to rigorous scientific standards can be challenging due to limited sample sizes impacting statistical power. Creating translational solutions can be additionally challenging when cultural and language differences exist. Therefore, researchers must learn to walk in two worlds. This paper explores practical and ethical dilemmas encountered when conducting research with small population groups in First Nations communities, and the opportunities afforded by privileging stories as data, and data as stories. Methods This study drew on experiences of co-researching with small groups of First Nations young people and Elders in diverse communities, to elucidate the importance of co-designing context-responsive methodologies and developing shared methodological language to achieve meaningful outcomes. While small samples typically produce less precise and generalisable findings, they can be particularly powerful for the communities involved and produce important findings with the potential to inform policymakers, service providers and practitioners to enhance population outcomes. Shared, iterative, reflective practice identified that conventional methods of research design and data analysis, and highly technical scientific language, were often not fit for purpose; therefore, innovative approaches are needed to progress urgent issues impacting wellbeing. Main findings Co-designing innovative methodologies that align with both Indigenous ways of knowing and scientific paradigms is both possible and powerful. Specifically, this study centred knowledge production on curating stories: the gathering of rich individual stories (idiographic design using mixed methods case studies) to generate high-impact knowledge; and systematically drew together a rich tapestry of many stories (idiothetic design using integrative analysis of case studies) to distil locally relevant cumulative wisdom and attain a bridge to more generalisable findings that inform theory development (as a more viable alternative to using nomothetic, large-scale research design). While individual stories were initially privileged as data, the importance of collective (larger scale) data as ‘community stories' was also found to be useful and accessible in a community context; data must be translatable as meaningful stories to guide action. Principal conclusions Drawing on mixed methods provided rich stories capturing both a breadth and depth of understanding of complex issues.
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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 | 0.306 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.073 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.008 | 0.010 |
| 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; 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".