Bibliographic record
Abstract
Could tending to weeds inform more reciprocal modes of occupying space? Noticing the mutual thriving of unlikely neighbours, this thesis learns from the resilience of undesirable ecologies to investigate how we might better grow together more sustainably. The entanglement of social and environmental needs is addressed by drawing on ruderal ecologies (a term for disturbance-prone plant communities) to develop an interdisciplinary and transcalar approach to architecture. Tools for noticing overlooked worlds are first created to learn specificity through modest site interventions in ruderal places. Like the dandelion rooted in a sidewalk crack, a reflective design project then imagines how unsealing paved grounds would reconfigure relations among human and nonhuman occupants in Ottawa, Canada. To find more hospitable grounds for growing and dying in common, a ruderal approach shows that ecotones of abundance can flourish among the gaps in things that matter, if tended to carefully.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".