Gesturing Towards Decolonial Futures
Bibliographic record
Abstract
In this article we review learnings from our collaborative efforts to engage with the complexities and challenges of decolonization across varied educational contexts. To do so, we consider multiple interpretations of decolonization, and multiple dimensions of decolonial theory and practice – in particular, the ecological, cognitive, affective, relational, and economic dimensions. Rather than offer normative definitions or prescriptions for what decolonization entails or how it should be enacted, we seek to foster greater sensitivity to the potential circularities in this work, and identify opportunities and openings for responsible, context-specific collective experiments with otherwise possibilities for (co)existence. Thus, we emphasize a pedagogical approach to decolonization that recognizes the role of complexity, complicity, and uncertainty.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".