Perspective changes everything: managing ecosystems from the inside out
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the past, environmental managers could behave as if they were managing a “natural” system to which they were external; criteria for successful management could be derived from historical data or from current pristine systems elsewhere in the world. With a few localized exceptions, this approach is no longer viable. Most of the ecosystems for which critical and urgent decisions need to be made are best seen as complex ecosocial systems, with people firmly embedded as an integral element. We can no longer manage ecosystems per se, but rather we must learn to manage our interactions within our ecological context. This view, which incorporates notions of multiple, interacting, nested hierarchies, feedback loops across space and time, and radical uncertainty with regard to prediction of system behavior, requires rethinking. How should we now think about science and science-based management? Post-normal science, complex systems theories, and the creation of collective narratives offer the best hope for making progress in this field. We use several ecosystem management and community health programs in Peru, Kenya, and Nepal to demonstrate the characteristics necessary for this kind of “inside-out” approach.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it