Rethinking Appropriateness of Actions in Environmental Decisions: Connecting Interest and Identity Negotiation with Plural Valuation
Bibliographic record
Abstract
Issues of interest, identity and values intertwine in environmental conflicts, creating challenges that cannot generally be overcome using rationalities grounded in generalised argumentation and abstraction. To address the growing need to engage interests and identities along with plural values in the conservation of biodiversity and ecological systems, we introduce the concept of ‘appropriateness of actions’ and ground it in a relational understanding of environmental ethics. A determination of appropriateness for actions comes from combining outputs from value elicitation with those of interest and identity negotiation in ways that are salient to specific people and their relationships to specific places. Drawing on the Blue Mountain Forest Partnership in the Pacific Northwest, we propose factors of success for supporting appropriate actions: 1) understanding context and identifying key stakeholders; 2) surfacing a diversity of interests and building system-level trust; 3) building empathy for different identities grounded in specific places; 4) eliciting diverse values and seeking to understand their links to worldviews and knowledge systems and; 5) seeking out appropriate actions.
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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 | 0.072 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.081 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| 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".