Community‐based observing for social–ecological science: lessons from the Arctic
Bibliographic record
Abstract
Environmental monitoring and observation by members of local communities have become increasingly common in the US and Canada over the past several decades. During the same period, social–ecological systems (SES) science has been developed to explain and predict human and environmental interactions, but empirical methods to generate matched social and ecological datasets are uncommon. Community‐based observing (CBO) methodologies were developed in the Arctic to allow for production of environmental data in a social context and are also well suited to provide empirical data for SES science. Community‐based observing networks and systems (CBONS) and community observer forums (COF) are methodologies developed from collaborations between community members, researchers, and government agencies. Here, we describe CBONS and COF methodologies, provide examples of their usage, and suggest ways in which they can benefit empirical SES science. We conclude by outlining efforts to expand the use of CBO to new knowledge and geographic domains.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".