Making sense of environmental values: a typology of concepts
Bibliographic record
Abstract
Debates about environmental values and valuation are perplexing, in part because these terms are used in vastly different ways in a variety of contexts. For some, quantifying human and ecological values is promoted as a useful technical exercise that can support decision-making. Others spurn environmental valuation, equating it with reducing ethics to numbers or "putting a price tag on nature." We make sense of these complexities by distilling four fundamental concepts of value (and valuation) from across the literature. These four concepts-value as a magnitude of preference, value as contribution to a goal, values as individual priorities, and values as relations-entail fundamentally different approaches to environmental valuation. Two notions of values (as magnitudes of preference or contributions to a goal) are often operationalized in technical tools, including monetary valuation, in which experts tightly structure (and thus limit) citizen participation in decision-making. This kind of valuation, while useful in some contexts, can mask important societal choices as technical judgments. The concept of values as priorities provides a way of describing individuals' priorities and considering how these priorities differ across a wider population. Finally, the concept of values as relations is generally used to foster deliberative forms of civic participation, but this tends to leave unresolved the final translation of civic meanings for decisionmakers. We argue that all forms of valuation-even those that are technical tools-constitute technologies of participation, and that values practitioners should consider themselves more as reflexive facilitators than objective experts who represent the public interest. We thus encourage debate about environmental values to pivot away from theoretical gridlock and toward a concern with citizen empowerment and environmental democracy.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.017 | 0.038 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".