A handbook for measuring the progress and outcomes of integrated Coastal and Ocean Management
Bibliographic record
Abstract
The handbook aims to contribute to the sustainable development of coastal and marine areas by promoting a more out-come-oriented, accountable and adaptive approach to ICOM. \nIt provides a step-by-step guide to help users in developing, selecting and applying a common set of governance, ecological and socioeconomic indicators to measure, evaluate and report on the progress and outcomes of ICOM interventions. \nIntended as a generic tool with no prescriptive character, the handbook proposes analytical framework and indicators that from the basis for the customized design of sets of indicators. \nThe handbook also includes results, outcomes and lessons learned from eight pilot case studies conducted in several countries. A network of ICOM experts in these countries has also been established. \nThe target audience is wide, and includes coastal and ocean managers, practitioners, evaluations and researchers. \nThe handbook forms part of an IOC toolkit on indicators. Its preparations is part of an effort to promote the development and use of ICOM indicators led by the Intergovernmental Oceanographic Commission, the Department of Fisheries and Oceans of Canada and the U.S. National Oceanic and Atmospheric Administration.
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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".