Unspeakable Things: Indigenous Research and Social Science. Révolutions, Contestations, Indignations
Bibliographic record
Abstract
Indigenous peoples have always done research. That is, they have always asked questions that mattered to them and they have always sought to answer them, mobilizing all relevant sources of knowledge. Only recently, however, has Indigenous research moved from Indigenous communities into the academy, challenging colonial institutional relations and practices that have constituted Indigenous peoples as objects of research rather than as authorities about their own ways of knowing, being and doing. In this article, we make the case for Indigenous research as made up of vital, transformative practices that emerge from and for Indigenous peoples, but that have at the same time profound implications for ‘ordinary’ social science. Moreover, we write with the avowedly political aim of participating in struggles to decolonize the social sciences. Yet despite the emergence of strong Indigenous voices within contemporary research there are both old and new threats to these voices in the academy. This is because Indigenous research is about knowledge production and application and both the philosophical and practical tenets of such knowledge subvert dominant social research. Too often, as Aileen Moreton-Robinson (2002:189) has argued, Indigenous knowledges are ‘unspeakable things’, not least because they challenge taken-for-granted assumptions that undergird the ongoing dominance of colonial practices of social science. Here we offer one account of Indigenous research in the academy, explaining the ways that Indigenous research acts as a form of resistance against centuries-old relations of colonization. At the same time, we explain shape shifting yet familiar threats to Indigenous resurgence, including specific attacks on Indigenous research.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.020 | 0.111 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| 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".