Biological Diversity, Ecological Stability, and Downward Causation
Bibliographic record
Abstract
INTRODUCTION The magnificent variety of life on Earth has astonished members of our own species ever since we arrived on the scene. Deep ecologists have honored this sense of awe by positing biological diversity as a moral end in itself. However, all of the other ethical paradigms considered by Oksanen (1997) treat biodiversity as only a means toward (and not a constituent of) “the flourishing of human and non-human life.” Nevertheless, most experts agree that whether diversity has any intrinsic value or not, it does have a myriad of key instrumental values. For example, it facilitates the “delivery” of “ecosystem services” such as carbon dioxide absorption, flood control, and nutrient cycling (Wilson and Perlman 1999). Important as their considered judgment is on this matter, scientists have only recently gotten around to testing it through experiment, theory, and systematic observation. One candidate mechanism is the contribution of species richness (number of species) to ecological stability. Presumably, more stable ecological systems provide ecosystem services more reliably. Given the ongoing wave of extinction wreaked by current forms of human economic activity, a revitalized research program on diversity–stability relations may seem to have come in the nick of time. However, it has encountered staunch resistance from certain quarters in ecology. In this chapter, I shall consider this research program, and challenges to it, in light of another debate, within philosophy and science: holism versus reductionism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".