Feeling Environmental Policing: Possibilities and Challenges for Socio-Ecological Justice.
Bibliographic record
Abstract
This is a conceptual paper that merges collaborative acts of storying with theoretical contributions from the affective turn (Clough, 2008) to illustrate ways by which mainstream forms of environmentalism (Klein, 2015) may inscribe normative ways of feeling and being with environments while policing others. Methodologically, we draw on our personal and collective storying-while-walking (Springgay & Truman, 2019) in and around the University of British Columbia (UBC) during the Canadian Society for the Study of Education 2019 conference. We consider how our encounters with/in nature are often disciplined by popular environmentalist discourses (e.g., recycling, greening, contaminating). In our walks/storying, we centre material agents (e.g., trash receptacles, kombucha bottle, tree) as part of affective economies (Ahmed, 2013) that align us to particular ways of feeling (with) nature, for example, embarrassment from not knowing how to recycle a kombucha bottle. We attune ourselves to this hegemonic environmental imaginary, in which certain humans assume control and dominion over nature and reinforce that control via green economies. This compels us to ask: in what ways do environmental efforts for cultivating more response-ability towards nature (Wallace, Higgins & Bazzul, 2018) come to exceed our response-ability with each other as part of nature? How might we follow affective economies that discipline how we value, manage and save nature, and how might this open up pedagogical possibilities for relating differently with each other/nature? With science and environmental education and research in mind, we suggest staying with emotions that make visible acts of environmental policing for socio-ecological justice.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.084 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".