Using the Framework Method to support collaborative and cross-cultural qualitative data analysis
Bibliographic record
Abstract
With Inuit organizations leading the way, there is a growing opportunity for meaningful partnerships between Inuit and visiting researchers to create impactful research programs and policy initiatives that reflect Inuit priorities. Collaborative research methods, where Inuit and visiting researchers work together to meet community needs, offer a potential avenue for braiding knowledge systems, and therefore have become an increasingly popular way to conduct research in the Arctic. In this paper, we outline our use of the data analysis method known as the “Framework Method” during the Imappivut Knowledge Study, a participatory mapping project led by the Nunatsiavut Government. We reflect on both the method's applicability and its usefulness for future research conducted in collaboration between Inuit and non-Inuit researchers. We find that the Framework Method allowed us to work in an iterative and adaptive manner, resulting in comprehensive findings for marine spatial planning. The method also supported data sovereignty for the Nunatsiavut Government. The Framework Method can be used to allow Nunatsiavut greater control over the data internally and self-determining access to external researchers.
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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 | 0.194 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".