Bibliographic record
Abstract
While holist views such as ecocentrism have considerable intuitive appeal, arguing for the moral considerability of ecological wholes such as ecosystems has turned out to be a very difficult task. In the environmental ethics literature, individualist biocentrists have persuasively argued that individual organisms—but not ecological wholes—are properly regarded as having a good of their own . In this paper, I revisit those arguments and contend that they are fatally flawed. The paper proceeds in five parts. First, I consider some problems brought about by climate change for environmental conservation strategies and argue that these problems give us good pragmatic reasons to want a better account of the welfare of ecological wholes. Second, I describe the theoretical assumptions from normative ethics that form the background of the arguments against holism. Third, I review the arguments given by individualist biocentrists in favour of individualism over holism. Fourth, I review recent work in the philosophy of biology on the units of selection problem, work in medicine on the human biome, and work in evolutionary biology on epigenetics and endogenous viral elements. I show how these developments undermine both the individualist arguments described above as well as the distinction between individuals and wholes as it has been understood by individualists. Finally, I consider five possible theoretical responses to these problems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.038 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| 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".