Community-engaged participatory climate research with the Pyramid Lake Paiute Tribe
Bibliographic record
Abstract
Climate change’s threat to the identity, culture, economy, and livelihoods of the Pyramid Lake Paiute Tribe (PLPT) can be better understood through community-engaged participatory methods. Our research team of Indigenous and non-Indigenous scientists formed a tribal-university partnership with the PLPT Council to understand how climate change and upstream pressures threaten PLPT ecosystems, lands, and resources. The objectives are to: (1) consider how decolonizing, Indigenizing, and participatory methodologies can inform climate research engagement between scientists and Indigenous partners; (2) understand PLPT perspectives of climate change impacts and priorities for climate research; and (3) engage the PLPT community in climate change discussion. Working with the PLPT Natural Resources Department, in accordance with PLPT research protocols, we convened a community-driven climate workshop in which environmental managers and community members identified environmental challenges, affected stakeholders, and potential solutions. The workshop participants emphasized the importance of water, culturally significant species, and the role of community in climate adaptation. These community-identified priorities highlighted the need to develop interpretive climate resources for community members, including a video summary of fish ecology. Overall, our collaboration with the PLPT benefited from greater community involvement, increased awareness of PLPT commitment to climate research, an iterative engagement process, prioritization of community perspectives, and incorporation of PLPT feedback on research outcomes. From our positionality as Indigenous environmental scientists, we conclude that decolonizing, Indigenizing, and participatory action approaches to climate research with Indigenous partners should strive for accountability to community research protocols and priorities; practical and useful outcomes; and empathetic and respectful engagement with research participants.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".