Comment on ‘In complexity we trust: learning from the socialist calculation debate for ecosystem management’
Bibliographic record
Abstract
Abstract Using a metaphor based on a historical debate between socialist and free-market economists, Salliou and Stritih ( Environ. Res. Lett. 18 151001) advocate for decentralizing environmental management to harness emergent complexity and promote ecosystem health. Concerningly, however, their account seems to leave little room for top-down processes like government-led sustainability programs or centrally-planned conservation initiatives, the cornerstone of the post-2020 biodiversity framework. While we appreciate their call for humbleness, we offer a few words in defense of planning. Drawing on evidence from ecology, economics, and systems theory, we argue that (1) more complexity is not always better; (2) even if it were, mimicking minimally-regulated markets is probably not the best way to get it; and (3) sophisticated decision support tools can support humble planning under uncertainty. We sketch a re-interpretation of the socialist calculation debate that highlights the role of synthesis and theoretical pluralism. Rather than abandoning big-picture thinking, scientists must continue the difficult work of strengthening connections between and across multiple social, ecological, and policy scales.
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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".