Navigating Murky Waters: Stakeholder Mapping to Inform Strategic and Effective Communications About Marine Protected Areas
Bibliographic record
Abstract
Marine Protected Areas (MPAs) are not a new concept but have taken center stage with the global commitments to protect 10% of the coast and ocean by 2020 and 30% by 2030. There has also been considerable growth in the fields of ocean literacy and ocean optimism, but it is not clear that this is translating into meaningful change "at sea level." National media polls in Canada have consistently demonstrated broad support for protecting 30% or more of the ocean. However, that same polling also suggests a lack of clear understanding and divergent views about threats to marine ecosystems, what an MPA is, and how they should be managed. If we are to successfully protect 30% of the ocean by 2030, we will need to rally public support and reduce opposition. The Canadian Parks and Wilderness Society (CPAWS), with support from Fisheries and Oceans Canada, has undertaken recent work to better identify and understand potential and priority audiences to build and activate support for marine protection at both national and local scales through extensive surveys and focus groups across Canada. The aim of this work is to provide a clearer picture of the values and levels of understanding related to MPAs, identify key audiences based on values, demographics, and geographic information, develop effective messaging that resonates with these audiences, and address common misunderstandings and misconceptions. We will present the results of this work and share next steps as we strive to bridge the gaps between scientific research, public understanding, and collective action.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".