Using auto-ethnography to bring visibility to coloniality
Bibliographic record
Abstract
This article traces how coloniality traps research and researchers in the Global North into maintaining the rigidity of its politics and logics through the meaning process. As International Social Work continues to gain popularity, supporting the proliferation of research across borders, the theoretical underpinnings must be unpacked with the context of the collaboration and the cultures involved that give meaning to both. The crux of the article rests within the implications for qualitative research in social work—both within, and across borders as a way of promoting social justice with marginalized communities. It also provides new possibilities for transcending and translating methodologies across the fields of social work and anthropology. To illustrate how research operates under the rubric of coloniality, this article uses autoethnography to uncover the on-the-ground realities of working across localities. The auto-ethnography revealed that despite the goal of sharing control of the research process, tensions related to coloniality emerged. As a result of working in different localities, each team’s processes became distinct—as it was informed by different historical, economic and geopolitical processes.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".