CLIMATE CHANGE ACTIVISM AND THE PEOPLE’S CLIMATE MOVEMENT
Bibliographic record
Abstract
This thesis explores the strategies and imaginations of activists working to inspire action on climate change. It is based on my ethnographic fieldwork with the Toronto People’s Climate Movement as well as my own experiences as an activist living and working in Toronto. In conceptualizing climate activists anthropologically, I understand social movement actors as connected by shared imaginations rooted in universalizing scientific discourses and defined by deeply-held concerns for climate change and the motivation to take action. I devise an explanatory schema for the climate movement based on the “code-sort” method that shows how the strategies of climate activists are informed by four key values: crisis mitigation, social change, collective organizing and individual agency. These values converge in different iterations to inspire a variety of activist strategies and imaginations. Some are about getting climate change onto the public agenda and emphasize the urgent need for top-down solutions to reduce emissions. Others work to factor personal autonomy and well-being into their goals and methods, taking as their starting point the understanding that, for solutions to climate change to be adopted by society at large, they must consider the needs of both people and the environment in their designs of sustainable systems. To begin to understand “how climate change comes to matter” (Callison 2014) demands that we venture into a different world; a world in which the threat of ecological catastrophe is not the elephant in the room but the guiding lens of every conversation; in which subject matter avoided in polite society is the focus of every planning meeting, potluck and PowerPoint presentation; in which the not-so-novel question of how to live a moral life meets the far-more-recent dilemma of how to live one that is also sustainable. In the imaginations of those who inhabit this world, climate change is everything.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.059 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".