Collaboration, Participation and Technology: The San Joaquin Valley Cumulative Health Impacts Project
Bibliographic record
Abstract
Community-university partnerships have been shown to produce significant value for both sets of partners by providing reciprocal learning opportunities, (re)building bonds of trust, and creating unique venues to formulate and apply research that responds to community interests and informs collaborative solutions to community problems. For such partnerships to be mutually empowering, certain design characteristics are necessary. These include mutual respect for different modes and expressions of knowledge, capacity-building for all parties, and an environment that promotes honest and constructive dialogue about the inevitable tensions associated with the interplay of power/knowledge. This article explores an innovative case of community-university partnerships through participatory action research involving a coalition of environmental justice and health advocates, the San Joaquin Valley Cumulative Health Impacts Project, and researchers affiliated with the University of California, Davis. In particular, we examine how participatory GIS and community mapping can promote co-learning and interdependent science. Keywords Community-based participatory research, environmental justice, Public Participation Geographic Information System
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| 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".