Trapped within the logic of modernity/coloniality
Bibliographic record
Abstract
Abstract Background Academic research is the site where the production and dissemination of knowledge are embedded in Eurocentric epistemologies which are posited as universal and non‐Eurocentric knowledges are devalued, dismissed, and ignored. Objectives The goal was to explore the tensions that emerged between the Global North team and the Global South team, from the Global North's perspective. Methods This paper a collaborative autoethnography to trace how the logics' of coloniality of power structured a transnational research project focused on exploring Indigenous Women's experience of marginalization. The autoethnography involved engaging in critical reflexive auto‐interviews between the team members in the Global North, analyzing field notes and personal written reflections. Results Autoethnography revealed how colonial conditioning respectively shaped both—the Global North and the Global South teams' expectations of one another, and as such, how they each operationalized decoloniality in the research process. Conclusion The Global North team focused on epistemically disengaging from coloniality but became overly concerned on meeting the expectations of the funder's temporally oriented productivity demands and ended up rearticulating coloniality's logics. The Global South was concerned with remedying the material dimensions of coloniality in their local community but became overly focused on adopting neoliberal logic models to efficiently satisfy the North's productivity expectations. Meaning, they, too, rearticulated coloniality's logics.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.074 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".