Rethinking biodiversity: from goods and services to “living with”
Bibliographic record
Abstract
Abstract Since the 1992 Convention on Biological Diversity, counting and mapping have come to dominate international debates around biodiversity protection. With the emergence of the Ecosystem Services concept, these counting and mapping efforts are increasingly imbued with an economic logic that argues that to save biodiversity, its goods and services must be given monetary value. This article offers a critical engagement with the Ecosystem Services discourse and the way it translates the diversity of nature into a single measure—a “currency”—to be included in systems of exchange. We argue that this conception of biodiversity is too narrow and potentially detrimental because it reduces biodiversity to a series of quantifiable fragmented parts that become liable to counting, mapping, and utilitarian use, and because it reduces social–natural relations to market transactions. Subsequently, we outline possibilities for conceiving and living with biodiversity that go beyond relations of counting, mapping, and commodification. It is important that biodiversity knowledge organizations, such as the recently sanctioned Intergovernmental science‐policy Platform on Biodiversity and Ecosystem Services (IPBES), take these into account. Conserving a diversity of life requires acknowledging a diversity of values, knowledge and framings of biodiversity, and fostering a diversity of social–natural relations.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.071 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".