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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.246 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it