Case Study of Collaborative Modeling in an Indigenous Community
Bibliographic record
Abstract
To support Indigenous communities in preparing for uncertainties such as climate change impacts and unexpected threats to health, there are calls by researchers and community members for decision support tools that meaningfully and sensitively bring together Indigenous contextualized factors such as social dynamics, local- and culture-specific knowledge, and data with academic tools and practices including predictive modeling. This project used a community engaged approach to co-create an agent-based model geographically bounded to a reserve community to examine three community-requested simulations. Community members and researchers co-designed, built, and verified the model simulations: a contaminated water delivery truck; a Pow Wow where a waterborne infectious disease spreads; and a flood which restricts typical movement around the reserve for daily tasks and health care. The simulations’ findings, displayed as both conventional and narrative outputs, revealed management areas where community adaptation and mitigation are needed, including enhancing health service provision in times of disease outbreaks or large community events, and creating back-up plans for overcoming flood impacts to ensure services are accessible for vulnerable members of the community. Recommendations for communities, researchers, and modelers are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".