Findings from the Series of Workshops “In Whose Backyard?—Exploring Toxic Legacies in Mi'kmaw and African Nova Scotian Communities”
Bibliographic record
Abstract
Currently, Mi'kmaw (Aboriginal) and African Nova Scotian communities throughout Nova Scotia, Canada experience disproportional effects of climate change, water contamination, waste disposition, and pollution from the nearby industries. Environmental health equity research findings show differential impacts of toxic facilities and other environmental hazards on health based on race and income. This results in significantly greater health risks for these communities relative to other communities that live in less exposed areas. The Environmental Noxiousness, Racial Inequities and Community Health (ENRICH) project was borne out of an interest in addressing the concerns that Mi'kmaw and African Nova Scotian communities share about the health effects of living near to toxic facilities and other environmental hazards. A series of workshops was held throughout Nova Scotia from September 2013 to January 2014 to discuss these concerns. The main purpose of these workshops was to identify residents' main concerns about the health effects associated with their proximity to toxic facilities and other environmental hazards and to obtain their suggestions for how a future research study could support advocacy efforts around environmental injustices in their communities. The workshop sessions included topics on past, current, and future advocacy efforts and community-based participatory action research. Outcomes from the workshops include consultations with key government departments, a workshop report, a documentary film, as well as communication resources for mobilizing the wider community, such as a project newsletter, a project website, Facebook, television, newspapers, radio, and community meetings.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".