Nanâtawihowin Âcimowina Kika-Môsahkinikêhk Papiskîci-Itascikêwin Astâcikowina [Medicine/Healing Stories Picked, Sorted, Stored]: Adapting the Collective Consensual Data Analytic Procedure (CCDAP) as an Indigenous Research Method
Bibliographic record
Abstract
Over the past several years, academic discourse has included discussions around improving research methodologies, particularly related to Indigenous people. Using Western research methodologies and methods when undertaking health research with Indigenous people, in the direction of Indigenous communities, has not been very effective. This is due to the fact that Western research methodologies do not address the need to foster relationships, mutual respect, and reciprocity. Engaging Indigenous communities empowers them to take an active role in how the research is conducted and ensures that the research is relevant to their communities. Engagement with Indigenous communities is also important during the analysis of qualitative data in the form of interviews, focus groups, and sharing circles. Without adequate engagement, data analysis often reverts back to Western methods, leaving the community out of the data analysis process. Bartlett et al. developed the “Collective Consensual Data Analytic Procedure” (CCDAP) in 2006 to address the lack of community involvement in the data analysis process. Analyzing the qualitative data using a community panel to reach a group consensus reduces the possibility of biases that any one person could bring to the research. Furthermore, group participation helps foster relationships and camaraderie within Indigenous communities. The process outlined by Dr. Bartlett could however become tedious and lengthy when dealing with a large number of interviews and data entries. This is why the CCDAP process was streamlined by first doing a thematic analysis of the data using the NVivo software. Following the thematic analysis, digitalization was added to the process by the way of Microsoft PowerPoint presentation and Excel spreadsheet. This made it quicker and easier to perform the analysis remotely using any videoconferencing platform that allows for screen sharing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".