An Indigenous data analysis framework of practice and Carmen’s Theory
Bibliographic record
Abstract
This article addresses a significant gap in our scholarly knowledge concerning the use of Indigenous ways of being, knowing, and doing, in research data analysis, when applying Indigenous philosophies and research methodologies. Often as Indigenous researchers when applying an Indigenous paradigm, standpoint, methodology and methods, we tend to revert to non-Indigenous western science approaches to the data analysis process, which was the case for first author Carmen when she was undertaking her Doctor of Philosophy and applied thematic analysis. To avoid conflicts between the use of the ologies , that is, ways of being, knowing, and doing, that are evident between Indigenous and western sciences we provide an Indigenist data analysis framework. This work adds value to scholarly knowledge by providing a way to maintain our Indigenous ways of being, knowing, and doing, during the data analysis research process, as we continue to restore our Indigeneity when applying an Indigenist paradigm and standpoint.
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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 | 0.139 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.011 | 0.093 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".