Decolonizing Methodologies in Qualitative Research: Creating Spaces for Transformative Praxis
Bibliographic record
Abstract
Though there is no standard model or practice for what decolonizing research methodology looks like, there are ongoing scholarly conversations about theoretical foundations, principal components, and practical applications. However, as qualitative researchers, we think it is important to provide tangible ways to incorporate decolonial learning into our research methodology and overall practice. In this paper, we draw on theories of decolonization and exemplars from the literature to propose four practices that can be used by qualitative researchers: (1) exercising critical reflexivity, (2) reciprocity and respect for self-determination, (3) embracing “Other(ed)” ways of knowing, and (4) embodying a transformative praxis. At this moment of our historical trajectory, it is a moral imperative to embrace decolonizing approaches when working with populations oppressed by colonial legacies.
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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 | 0.434 | 0.369 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.014 | 0.103 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".