re:TO: Pursuing Urban Re-Imaginaries Through an Affected Ontological Inquiry into the Capitalocene In Toronto
Bibliographic record
Abstract
This project embraces the more-than-human-turn by building upon two concepts, one from the environmental humanities and one from climate communications: from the environmental humanities, the Capitalocene thesis; and from climate communications, the localisation concept. These two theories are brought together in a praxis project utilizing walking and autoethnographic research methodologies in an affective, ontological inquiry into the researcher's experience of Capitalogenic climate change within her city, Toronto. The hypothesis states that disrupting everyday patterns of city life through critical sensory walking inquiry into place creates potential for localising Capitalogenic conditions, thereby further creating cognitive space to reimagine possibilities within her daily life in Toronto. Hypothesis is guided by three research questions - (1) How does the researcher, a Torontonian, perceive Capitalogenic climate change conditions within the city?, (2) Can walking research methodologies be used for reimagining urban realities during the Capitalocene?, and (3) Can walking-as-localising research be a means to inspire mitigation and adaptation efforts among other Torontonians? Documentation of autoethnographic processes on the website re-TOronto.com becomes a model for citizen walking research during Capitalogenic climate change in Toronto.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".