The multiple values of nature show the lack of a coherent theory of value—In any context
Bibliographic record
Abstract
Abstract Pathways to sustainability require a broader and fuller representation of the multiple values of nature in policy and practice. In this People and Nature special feature entitled ‘The Multiple Values of Nature’, researchers interpreted all three key words differently: multiple, values and nature. The articles also engaged variously with concepts, theory, practice and data. In the face of this diversity, some see a burgeoning field and others see a mess. In this editorial, we characterize the diversity of these contributions and consider whether the field is poised to become mainstream. Specifically, we ask what might be limiting its efforts to unsettle the dominance of economic valuation. Like the broader field, the articles engage little with theory, and only one paper engaged with a theory of value (the dominant ‘utility theory’, rejecting a component of it). All articles thus seemed dissatisfied or disengaged with existing theories of value; this suggests that popular theories of value cannot properly account for the diversity of ways that people value and relate to nature. Perhaps there is a fundamental lack in how we understand value in any context (not just nature). As this fledgling field matures, we argue that building theory is key. Specifically, there is a need to articulate a theory of value to accommodate the multiple values of nature, which relates the various concepts to empirics, and which serves as a foundation to guide practice. To facilitate this theory development, we outline a set of ways that a new theory of value would need to differ from the dominant economic (utility) theory of value in order to explain what is known about the multiple values of nature. Whether by illustrating and enlivening an existing alternative theory of value or by inspiring a new theory, perhaps this fledgling field of the multiple values of nature is poised to disrupt much broader understandings of what matters to people and why. 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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".