Imagining Tree Futures of Ottawa: Climate Change, Activism and Politics in the Urban Forest
Bibliographic record
Abstract
This dissertation traces the ways trees, activists and institutional actors like the City of Ottawa and the NCC practice "tree imaginaries": speculative place-making acts that seek to define and control possible futures of Ottawa and its urban forest.Trees become participants in activist imaginaries of a more treed future and become climate change mitigating infrastructure in municipal urban forest management regimes.I follow activists at three different sites where trees are at risk of being cut down for development or because they are not conforming to municipal expectations of how trees should exist in an urban setting.Activists and institutional actors saw trees as providers of important climate mitigation services.However, as activists spent more time in the treed places they were seeking to protect, they developed affective relationships with the trees in question, complicating their role as inert, purpose-built infrastructure.In the first half the dissertation, I examine the role trees have played historically in Ottawa's landscape.Then demonstrate how activists, initially willing participant in institutional tree planting and protection plans, became disillusioned with institutional inaction and obfuscation as they tried to advocate for the trees they cared for.In the second half, I argue that emotional relationships with trees lead to new appreciation and knowledge about them, as well as new visions for their future.I conclude by analyzing activist artmaking as tactics for getting institutional attention, as practices that create new communities of "tree people," and as analytical tools for developing new understandings of trees and tree agency.A critical outcome of this research has been to show that for climate action to incorporate more than just human perspectives requires new visions of the future.Furthermore, I argue that, trees, when acting as urban infrastructure, become part of a fantasy of disposable time, that presumes it is possible to forestall climate change's worst effects without addressing its root causes.Lastly, I experimented with artistic methods for doing ethnography with trees, coming away with the recognition that tree survival depends on all the elements, histories, animals and plants that contribute making tree places.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.040 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".