Local empowerment through the creation of coastal space?
Bibliographic record
Abstract
Developments in national fisheries and marine environmental policies during the last 30 years have changed the relationship between coastal communities and the marine resources that people in these communities traditionally harvested.In Norway, for example, when the state authorities have made decisions to defend what they regard as national interests, the local level has been left with authority over minor issues related to area planning in the coastal zone.Although coastal planning until recently was about sharing fishing areas between different users, we now see a spatial dimension emerging in planning, giving it a much broader scope.The processes of defining spatial properties and creating coastal space as a governable object have the potential to empower local communities.These processes contribute to enhanced local control and improved local participation in the governance of natural resources.In Norway, the 2008 Planning and Building Act strengthened the role of municipalities in local planning.In addition, the application of a new three-dimensional, spatial approach to coastal planning may create opportunities for new control over local resources.In marine spatial planning (MSP) the natural resources are seen as part of coastal spatial properties; thus, governing of sea space implies resource governance.As our examples illustrate, considerable power is associated with the ability to identify and define the properties of coastal space.MSP could become an important tool for controlling local resources, rebuilding collapsed fisheries, and managing them sustainably at the level of municipalities.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".