Epistemic justice in flood-adaptive green infrastructure planning: The recognition of local experiential knowledge in Thorncliffe Park, Toronto
Bibliographic record
Abstract
Evidence shows that green infrastructure planning relies on technocratic and economic valuation approaches to protect lands of high monetary value against flooding without considering climate justice. Contrasting epistemic justice against recognition and socio-cultural valuation of ecosystem services, this study explores how flood-adaptive green infrastructure planning may recognize and include the local experiential knowledge of under-represented groups. We focus on Thorncliffe Park, a dense tower neighborhood with a low-income immigrant population in Toronto, Canada, to explore: (1) the local experiential knowledge of residents about floods, climate-adaptive green infrastructure, and structural vulnerabilities; (2) the root causes of epistemic injustice in previous adaptive green infrastructure interventions; (3) siting options for future adaptive green infrastructure. The methodology includes 199 online surveys -among Thorncliffe Park's residents and 20 in-depth interviews with local community leaders and Toronto-based planning experts, policy reviews. Additionally, a spatial component consists of 120 online participatory mapping activities and spatial analysis of surface run-offs. Our findings -reveal that Thorncliffe Park residents are excluded from adaptive green infrastructure planning because flood management -remains a technocratic process -grounded in economic valuation approaches and technical justifications. Our findings -indicate that decision-makers have not credited residents’ needs and testimonies over decades due to -historical racial and socio-economic prejudice toward Thorncliffe Park's residents. We also -identify four hermeneutical barriers that prevent residents from impacting decisions, namely a lack of: social networks, citizenship rights, climate awareness opportunities, and communicational tools.
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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 | 0.000 | 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".