Resisting scientific extractivism: A post-extractivist policy of knowledge production with marginalized communities
Bibliographic record
Abstract
This article analyses scientific extractivism as a research process in which the experiences, discourses and knowledge of members of marginalised social groups are subalternised, i.e. reduced to raw data appropriated by academics. What has been captured and assimilated is then largely reinjected into closed circuits operating essentially between academics, from which marginalised communities are largely excluded. Ultimately, extractivism produces scientific careers and minefields; it confers disproportionate benefits to academics and little or no benefit on communities in material support, intellectual credit, or contribution to social struggles, which may lead them to turn away from academia. This analysis then raises the importance of developing post-extractivist approaches in the social sciences, based on an ethics of knowledge production rooted in the concepts of epistemic justice, reciprocity and accountability. I introduce a set of post-extractivist research postures and practices: clarifying and negotiating expectations of research projects; promoting a relational ethics on issues of epistemic and social justice in the production of knowledge with communities; countering the subalternisation of knowledge by reconsidering the teaching of qualitative methodologies in the social sciences; valuing reciprocity and accountability towards communities; and reconsidering the logic of careers and the functioning of our academic institutions. This analysis is based on pioneering work on this subject, particularly in a context of the relationship between the Global North and the Global South, such as those of Rivera Cusicanqui (2010), Tuhiwai Smith (2012), Betasamosake Simpson (Klein 2013), Gudynas (2013) or Grosfoguel (2016a, 2016b). They are also informed by my experience in participatory research with community-based organisations that work with marginalised communities in the field of the fight against poverty, homelessness and mental health in Quebec (Canada).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".