Sensing policy: engaging affected communities at the intersections of environmental justice and decolonial futures
Bibliographic record
Abstract
Pushing back against an extractive approach to research to center relationships, this paper draws from ethnographic sensibilities and community vignettes to discuss what academic-activists and political scientists can learn from communities’ situated bodies of knowledge. Tensions emerge when those most directly affected by public policy decisions are excluded from the decision-making process. Consultation leaves many encountering a paradox: their lived experiences are discredited even when they are invited to participate. This paper offers an imaginative approach to the design of participatory policy processes and asks: how can decision-makers meaningfully engage affected parties in pursuit of environmentally just policy creation? In response, this paper argues that bodies generally, and guts specifically, are political. To do so, I flesh out how a sensing policy approach to public engagement and socially engaged research can assist those crafting policies – including, laws, programs and service-delivery – to address contentious multilayered environmental justice issues. These include concerns for more-than-human life. Reflecting on experiences of community-engagement with Indigenous communities in Canada and Hawaiʻi, sensing policy builds from interpretive methods and intersectionality-based policy analysis to inform and potentially improve decision-making processes by taking seriously the experiences, knowledges and voices of those most affected by the government (in)decisions.
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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 | 0.034 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.073 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".