North End Narratives: Grid-Group Analysis for Environmental Justice in Hamilton, Ontario
Bibliographic record
Abstract
This paper explores environmental inequality and perceptions of environmental risk among people living in proximity to the industrial sector of Hamilton, Ontario (Canada). This sector is adjacent to Hamilton’s lower city, where on average socioeconomic status is low and rates of poverty, ill-health, and exposure to air pollution are high compared to the upper City of Hamilton (“the Mountain”). Using interviews with lower Hamilton residents and local environmental activists and ethnographic data, I seek to assess whether the grid-group and Cultural Theory approaches developed by anthropologist Mary Douglas are suited as tools for recognizing and analysing perceptions of environmental risk among Hamiltonians and making visible populations or cultural views that may be overlooked otherwise. I also assess grid-group and Cultural Theory as means for improving risk communication and informing public policy-making regarding environmental health hazards. I conclude that grid-group and Cultural Theory can serve as valuable tools for making visible some of the social influences on risk perception, but also identify drawbacks of the classificatory nature of Cultural Theory. As such, this paper contributes to the existing literature on environmental risk and offers an exploratory approach to this topic by using grid-group and Cultural Theory as a framework for conceiving of risk.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".