Economizing Nature as a Political Strategy: Is It Working?
Bibliographic record
Abstract
The idea that we need to “sell nature to save it” has become somewhat of a truism in conservation discussions. Financial flows change the world, the argument goes, and if conservationists can alter those flows, they can change the world. This has led, in recent decades, to collaborations among ecologists, economists, and governments in attempts to mainstream biodiversity and ecosystem services into a variety of economic framings and tools. By bringing biodiversity into the domain of economic calculus, perhaps the inherently enterprising capacities of nature can be valued and preserved. In other words, by extending the market to include biodiversity, nature should save itself. Enterprising Nature is the first book by Jessica Dempsey, an assistant professor in the Department of Geography at the University of British Columbia. In the book, Dempsey draws on over 10 years of research into global biodiversity politics to offer a fresh perspective to these ever-important debates about the financialization and commodification of nature. In simple terms, Dempsey sets out to evaluate whether “selling nature to save it” is actually working as a political strategy. By tracing the networks of people and ideas that have influenced conservationist arguments to commodify nature, Dempsey takes readers through a cumulative series of choices made by scientists and their collaborators that have resulted in a framing of the conservation “problem” within a market-based model. In so doing, she provides a window into a room that many of us have long inhabited but whose dimensions and dynamics we have never seen so clearly. Throughout this account, Dempsey points to other ways of framing local and global biodiversity that have been rejected and marginalized along the way. By revisiting these choices and their alternatives, she argues, a new global biodiversity politics can be envisioned and, perhaps, pursued.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".