Bibliographic record
Abstract
The classical problem of natural evil holds that the suffering of sentient beings caused by natural processes is an evil for which a divinity is morally responsible. Theodicies either explain natural evil as a punitive imperfection in nature, which humans ought to avoid and/or purify, or as a constituent part of a greater good whereby the evil is redeemed. The environmental ethics literature has taken the latter route with respect to the secular problem of natural evil, arguing that local disvalues such as predation or pain are transmuted into systemic-level ecological goods. The anti-hunting literature takes the former route, arguing that humans should not participate in the predatory aspects of the natural order. The anti-predation literature, furthermore, argues that nature should be redeemed – so far as is technologically and economically possible – of its unsavoury predatory aspects. While all sides of the debate employ strategies analogous to those found in the philosophy of religion, the immanentizing function of secularism moves the target of ultimate moral evaluation away from the divine and onto the natural. Environmental ethics’ teleological approach culminates with nature as a transcendent good, whereas anti-hunting and anti-predation critiques view nature in the here-and-now as riven with evil, requiring humans to distance themselves while decontaminating it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.046 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".