The ENRICH Project: Blurring the Borders between Community and the Ivory Tower
Bibliographic record
Abstract
In the spring of 2012, the author agreed to direct a project on environmental racism in Nova Scotia after meeting with Dave Ron, a social and environmental activist who had been involved for some time in the Save Lincolnville Campaign, a community-led initiative for the removal of the landfill near the African Nova Scotian community of Lincolnville. Thirsty for a new challenge that had the potential to effect real change in racially marginalized communities, she understood that the significance of the project lay in its uniqueness: few, if any, studies exist that examine environmental racism in both the Indigenous and Black communities in Canada. Given the dearth of research on environmental racism in Nova Scotia, particularly from the perspectives of these two communities, the project serves as a kind of case study for telling a particular kind of story situated in the Nova Scotian context and, in many cases, in the larger Canadian context.That project, which was later titled the Environmental Noxiousness, Racial Inequities and Community Health (ENRICH) Project, is a community-based academic study of the socioeconomic and health effects of environmental racism in African Nova Scotian and Mi’kmaw communities. From its inception, the mission of the ENRICH Project has been to employ an interdisciplinary, multi-methodological approach that bridges the academy and community to support ongoing and new efforts by Mi’kmaw and African Nova Scotian peoples to address the social, economic, political, and health effects of disproportionate pollution in their communities.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".