Moving from ecosystem-based policy objectives to operational implementation of ecosystem-based management measures
Bibliographic record
Abstract
The aim of ecosystem-based management (EBM) is to maintain an ecosystem in a healthy, productive and resilient condition through the implementation of policies and management measures. Although cross-sectoral planning may be led by a planning competent authority, it is up to the sector competent authority to implement the necessary management measures within their operations to achieve EBM goals and objectives. We suggest that scientific impediments to EBM are no longer significant to implement EBM operationally. Instead, we consider that approaching EBM within current policy cycle approaches would provide the necessary policymaking process step to operationalize EBM. In addition to enabling and facilitating collaboration, exchange, understanding as promoted by EBM, policymaking processes also require that policy is to be implemented through programs, measures, procedures and controls that have expected outcomes to “carry into effect” the policy objective. We are of the view that moving EBM from planning and objective setting to operational implementation is a management problem solving issues instead of a scientific one.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".