A Global De-colonial Praxis of Sustainability — Undoing Epistemic Violences between Indigenous peoples and those no longer Indigenous to Place
Bibliographic record
Abstract
Addressing our growing planetary crisis and attendant symptoms of human and human-ecological disconnect, requires a profound epistemological reorientation regarding how societal structures are conceived and articulated; named here as the collective work of decolonisation. While global dynamics are giving rise to vital transnational solidarities between Indigenous peoples, these same processes have also resulted in complex and often contradictory locations and histories of peoples at local levels which unsettle the Indigenous–non-Indigenous binary, providing new and necessary possibilities for the development of epistemological and relational solidarities aimed at increasing social–ecological resilience. The International Resilience Network is an emerging community of practice comprised of Indigenous and settler–migrant peoples aimed at increasing social–ecological resilience. This article narrates the story of the Network's inaugural summit, and provides an overview of contextual issues and analysis of particular pedagogical aspects of our approach; foregrounding ruptures between ontology and epistemology that inevitably occur when culturally and generationally diverse groups who are grounded in different daily realities and related agency imperatives come to share overlapping worldviews through learning ‘in place’ together. Developing pedagogical practices for naming and negotiating associated tensions within the collective work of decolonisation is, we argue, a critical step in enabling practices conducive towards the shared goal of increased human–ecological resilience.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| 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".