Mapping for change: Balancing big and small data in documenting environmental (in)justice in a small Canadian city
Bibliographic record
Abstract
Abstract This article recounts the development of an ongoing community‐engaged research project that maps environmental injustice in Peterborough/Nogojiwanong, Ontario. Drawing insight and inspiration from the activism and scholarship in environmental injustice in Canada and a specific understanding of environmental injustice as slow violence, this project seeks to understand how community members experience environmental risk. In this article we explore two spatial methods we undertook to understand the landscape of environmental inequality in Peterborough: the development of an environmental injustice index using GIS mapping, complemented by participatory mapping with community members. In presenting the results of these interlinked mapping efforts, we make the argument that while both methods centred community needs, either taken alone would be insufficient to understand the complexity of environmental injustice in Peterborough. Instead, we call for environmental justice projects that combine both big and small data for research that is critical, community‐engaged, and focused on justice.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".