Mapping Environmental (In)Justice Using Public Participation GIS (PPGIS) in Nogojiwanong/Peterborough
Bibliographic record
Abstract
Environmental injustice is not only a question of where harms are located but also of whose knowledge is recognized and whose voices shape responses. In Peterborough, Ontario, marginalized communities continue to experience uneven exposure to environmental risks alongside fragile infrastructures of care and belonging. Municipal governance, while increasingly reliant on technocratic tools like indices and standardized datasets, often misses these lived dimensions of injustice. This study investigates how environmental justice can be more fully understood by bringing together an Environmental Justice Index (EJI) with participatory mapping conducted through a year-long series of community workshops. Twenty residents documented environmental harms and benefits through shared map, photographs, reflections, and group dialogue, producing a textured account of slow violence, ecological care, and spatial exclusion. By reconciling these forms of knowledge, this research demonstrates the value of environmental data justice and combining big and small data to expose inequities, amplify community expertise, and inform more accountable governance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".