Humanity’s Domination of Nature is Part of the Problem: A Response to Kareiva and Marvier
Bibliographic record
Abstract
In “What is conservation science?”, Peter Kareiva and Michelle Marvier (2012) argue that “human domination is now so widespread and profound that it can no longer be ignored in any conservation decision” (p. 965). They note that in recent decades, human populations and the per capita consumption of energy and materials have increased immensely, whereas “managed ecosystems increasingly dominate the planet” (p. 964) because of ever-expanding human economies. Their article raises a key question: Does true conservation require humanity to set limits to our domination of nature? Kareiva and Marvier answer this question in the negative. None of their “normative postulates” involves limiting human demands on the biosphere, either as a matter of justice toward other species or as prudent self-interest. Conservation centered on keeping lands wild is “socially unjust” (p. 965), they assert, since it may move people off the land or reduce their economic opportunities. At no point do the authors acknowledge that people ever act unjustly by displacing other species or degrading their habitats, through road building, urban sprawl, farming new lands, and so on. Their ideology appears to reflect anthropocentric bias grounded in human exceptionalism.
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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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.065 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.042 | 0.055 |
| Insufficient payload (model declined to judge) | 0.002 | 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".