Bibliographic record
Abstract
My thesis is about the governance of trees through what seem to be natural configurations of order, and about how this form of tree governance is utilized to legitimize the governance of certain humans. By interpreting a series of in-depth interviews and participatory observations (n=84), and through analyzing government documents, laws, and regulations, my thesis makes visible how technologies of power operate through practices of landscaping and, in particular, how they manifest through utilizing ecological narratives that revolve around the tree. The thesis is divided into two main parts. The first part (chapters 1 to 3) pursues an exploration of tree ideologies in Israel/Palestine, and is mostly structured around the binary juxtaposition of landscapes of pine against olive. The second part (chapters 4 and 5) examines the governance of trees in four North American cities: Toronto, Vancouver, Boston, and Brookline. In particular, chapter 1 focuses on the ideological construction of the pine as the Jewish tree. This massive enterprise ensures not only a lush, environmentally-sound, and Eurocentric landscape, but also a means for physically occupying land without directly using humans. Chapter 2 of the dissertation focuses on the configuration of the olive tree as a symbol of Palestinian nationalism and resistance, and the counter-attempts to interrupt this configuration. Chapter 3 examines the nexus of law, nature, and technology in the construction of the West Bank landscape. The urban part of the thesis revolves around the war between Nature and the City, between humans and nonhumans, and between pipes and roots. Specifically, chapter 4 illustrates that practices of city treescaping serve city officials to funnel human traffic, promote community health, and police crime. Finally, chapter 5 explores the liminal spaces that thrive in the cracks of "tree culture," exposing the hybrids and networks of everyday street life such as grids, grates, and the Dig-Safe procedure. This exploration goes to suggest that, in certain respects, it is not only humans that govern trees but also trees that govern humans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".