Imagining an ecological right to the city in Toronto through drama-based research
Bibliographic record
Abstract
This article explores the possibilities in harnessing drama-based research methodologies in examining youth’s ‘right to the city’ during the COVID-19 pandemic and the climate emergency [Lefebvre (1996). Writings on Cities. Cambridge, MA: Blackwell Publishers]. Using ‘auto-topography’ [Heddon (2007). “One Square Foot: Thousands of Routes.” PAJ 29 (2): 40–50. doi:10.1162/pajj.2007.29.2.40] as a drama-based methodological prompt, the research considers how the right to the city is summoned, imagined and articulated by youth in one virtual Grade 6 classroom amid the alienation and isolation of a COVID lockdown in Toronto in November 2020. Specifically, this article attends to how auto-topography brought the ‘real’ and ‘imagined’ city into the virtual classroom, via Deleuze and Guattari’s [(1985). “Kafka: Toward a Minor Literature: The Components of Expression.” New Literary History 16 (3): 591–628] concept of ‘the minor,’ to contest majoritarian constructions of ‘nature,’ ‘culture,’ and urban citizenship. In particular, such ‘minor’ desires for the city, made appreciable by the imaginative and affective capacities of theatre and performance genres, gesture towards a politics of co-flourishing, where enchantment, generosity, gratitude, strangeness, surprise and hilarity– and, crucially, obligation and reciprocity – are integral to youths’ right to the city in these times of pandemic and ecological instability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.038 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".