Bibliographic record
Abstract
Abstract Charles, A., and Wilson, L. 2009. Human dimensions of Marine Protected Areas. – ICES Journal of Marine Science, 66: 6–15. Planning, implementing, and managing Marine Protected Areas (MPAs) requires that attention be paid not only to the biological and oceanographic issues that influence the performance of the MPA, but equally to the human dimensions: social, economic, and institutional considerations that can dramatically affect the outcome of MPA implementation. This paper explores ten human dimensions that are basic to the acceptance and ultimate success of MPAs: objectives and attitudes, “entry points” for introducing MPAs, attachment to place, meaningful participation, effective governance, the “people side” of knowledge, the role of rights, concerns about displacement, MPA costs and benefits, and the bigger picture around MPAs. These people-orientated factors and their impact on the success and effectiveness of MPAs are examined in relation to experiences with MPAs globally, and in relation to two Canadian examples specifically, one coastal (Eastport, Newfoundland) and the other offshore (the Gully, Nova Scotia).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".