Bibliographic record
Abstract
In this article, the author argues that ecosystem management is a policy choice masquerading as an inevitability. Ecosystem management is a process that measures, controls and changes ecosystems to produce the most desirable environment in human terms. The article begins with a discussion of two developments from which ecosystem management derives its legitimacy, the theory of nonequilibrium in ecosystems and the extinction of pristine systems: ecosystems exist in a fluid and dynamic state, and there are no ecosystems that are completely unaffected by human impact. Therefore, according to the prevailing view, it is not possible to preserve ecosystems in a natural state. The author questions the logic of that conclusion, arguing that neither nonequilibrium nor the absence of pristine systems dictates that ecosystems must be controlled and deliberately changed. The article's contention is not that natural is preferable, but that it is possible, and that the debate between ecological preservation and environmental utilitarianism can and should occur. If science and law dictate that there are no options but to deliberately change ecosystems, as the managers believe, then the debate has no relevance. Thus, the thesis is not that ecological preservation is a better choice than ecosystem management, but that there is a choice to make.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.066 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".