Wounds on This Turtle’s Back: On Feeling Extractivism and Felt Theories of Change
Bibliographic record
Abstract
This article is concerned with feeling the effects of global extractivism, with particular emphasis on the afterlife and ongoing threat of extractive industrial development in Ontario. Situated in Indigenous feminist thought and informed by Cree and Anishinaabemowin languages, this article positions the Earth as a living being, a loving Mother who intimately experiences the violences of extraction. Through Indigenous epistemologies, the authors challenge dominant settler colonial constructs that segment feeling, thought, doing, and being. In framing the impacts of extractivism as material evidence of violence, as wounds that are felt, the authors consider what feeling in relation has to do with environmental justice. They weave together Indigenous languages, stories, and philosophies to underscore the intertwined nature of thinking and felt experience, emphasizing the importance of the affective in doing things differently. Indigenous environmental justice in the push for and in the wake of global extractivism is not a struggle against human annihilation but, rather, encompasses whole structures of care rooted in love, respect, and reciprocity for all beings. Such an understanding of justice demands a collective reawakening and calls for more than just survival but good life for all. This echoing call desires a return to ways of being, knowing, feeling, and relating, which have been under attack at the same time as extractive industries bore ever further into the land. Yet the Earth continues to care for everything, while feeling and holding the weight of this world; she/they persist(s) in loving fully and giving endlessly, wanting only for there to be good life in return.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.104 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| 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".