Inclusive approaches for cumulative effects assessments
Bibliographic record
Abstract
Abstract The cumulative impacts of human activities and natural disturbance are leading to loss and extinction of species, ecological communities and biocultural connections people have to those ecosystems. Exclusive and extractive western science methodologies often hinder the inclusion of Indigenous knowledge holders in cumulative effects assessments (CEAs), which can lead to regional conflict and ineffective assessment and management of cumulative effects. We offer our reflections on the development of a collaborative CEA process with the Kitasoo Xai'xais, Nuxalk and Wuikinuxv First Nations in what is now known as the Central Coast of British Columbia. We designed our CEA around the guiding principles of respecting Indigenous sovereignty and regional autonomy, designing for trauma‐informed approaches, and prioritizing inclusivity and reciprocity. We focused our efforts on identifying current and future pressures on species of the Nations' choice. We relied on expert elicitation and data‐driven approaches to identify and map current and future cumulative impacts to predict their consequences for species' health. We used combinations of visualizations, numerical, oral and written materials to convey, elicit and share complex information with experts. We found a diversity of elicitation processes fostered the involvement of a variety of experts (e.g. Indigenous knowledge holders and Nation staff, regional biologists, Crown managers, tenure holders). We mapped over 90+ impacts to species in the region and after conversation and facilitated elicitation processes with over 50 knowledge holders, emerged with predictions for the consequences of seven plausible scenarios of future cumulative impacts for eight species as well as broad themes for the management of cumulative impacts to the lands and waters of the Nations with whom we collaborated. Our shared lessons can support researchers, planners, proponents, and Indigenous and colonial government agencies to conduct inclusive, collaborative and accessible CEAs that inform regional land and marine use planning. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.047 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".