The Pardy-Ruhl Dialogue on Ecosystem Management Part V: Discretion, Complex-Adaptive Problem Solving and the Rule of Law
Bibliographic record
Abstract
The question of discretion in ecosystem management cuts to the core of the way one conceives environmental law. How much discretion is appropriate? What kind? In whose hands? Discretion is particularly troublesome when there are no generally applicable, abstract rules. EM is a dynamic, process that consists of a continual series of actions and measurements that adjusts solutions to changing conditions, rather than a one-time decision about relative rights and responsibilities like a judicial decision. In spite of these differences, EM is more like a conventional decision-making system than a complex-adaptive system because it is coercive. From the perspective of ordinary citizens, EM is a prescriptive phenomenon. It tells them what to do. It consists of an authority giving orders - and not even in a manner in which the authority can be held to democratic account or legal appeal. Its legal power comes from the centre; its authority is vested in a scientific elite; managers have the power to compel a plan of action. These managers are generally competent professionals with the best of intentions, but the rule of law is not based upon faith in good intentions. It is achieved by limits to discretion and structural checks and balances. The case against discretionary ecosystem management is not that legal traditions are more valuable than ecosystem integrity, but that limits on discretion are more likely, not less, to protect such integrity. A discretionary, ad hoc administrative process is not the mechanism that will halt an incremental slide into a completely human-made environment. EM infringes upon liberties, breaches legal norms, gives control to unaccountable authorities and yet still fails to stem the tide of ecosystem decline. The onus is on the managers to show that EM is now the only possible approach to ecosystem governance. Until then, we should look for something better.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".