Ecological Historicity, Novelty and Functionality in the Anthropocene
Bibliographic record
Abstract
While many recognise that rigid historical and compositional goals are inadequate in a world where climate and other global systems are undergoing unprecedented changes, others contend that promoting ecosystem services and functions encourages practices that can ultimately lower the bar of ecological management. These worries are foregrounded in discussions about ‘novel ecosystems’ (NEs), where some researchers and conservationists claim that NEs provide a license to trash nature as long as certain ecosystem services are provided. This criticism arises from what we call the ‘anything goes problem’ created by the release of historical conditions. After explaining the notion of NEs, we identify numerous substantive motivations for worrying about the ‘anything goes problem’ and then go on to show that the problem can be solved by correcting two mistaken assumptions. In short, we argue that the problem is a product of adopting an overly sparse functional perspective that assumes an unrealistically high degree of convergence in the trajectories of natural processes; our analysis illuminates why such assumptions are unwarranted. Further, we argue that adopting an appropriate ethical framework is essential to overcoming the ‘anything goes problem’, and suggest that a certain virtue-ethics conception of ecological management provides useful resources for framing and resolving the problem.
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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 | 0.003 | 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 | 0.004 | 0.057 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".