Bibliographic record
Abstract
Managing social-ecological systems can be daunting because of numeric and dynamic complexity. These complexities present great uncertainties for scientists, policy makers, stakeholders, and other groups. When approaching complicated problems, there are often mismatches between problems and solutions. At least three caricatures are useful in demonstrating the mismatch between problem and solution sets [See ADDENDUM]. For simple problems such as making a meal, a cookbook or recipe approach suffices. Other classes of complex problems are amenable to engineering approaches. For example, building bridges, sending men to the moon, or constructing trustworthy aircraft not only rely on a combination of optimization and efficiency to deal with limited resources but also call for functional redundancy to maintain system stability and reliability. The class of environmental issues and problems discussed in this journal and other outlets is much more complex and subject to true uncertainty and surprise, indeed, much more like raising a child. We argue that this class of problems requires novel approaches, creative combinations of strategies, and the ability to adapt in a changing environment.
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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".