<scp>G</scp>reat expectations: Examining the designation effect of marine protected areas in coastal Arctic and sub‐<scp>A</scp>rctic communities in Canada
Bibliographic record
Abstract
Abstract Marine protected area (MPA) proponents suggest that local communities can benefit from the designation effect that ensues from the creation of protected areas. Few studies, however, have examined the designation effect of MPAs. In order to illustrate the existing and potential designation effect of MPA establishment in Northern Canada, case studies and exploratory research instruments consisting of the following indicators—(i) International Union for Conservation of Nature (IUCN) protected area category; (ii) natural heritage (wildlife, landscapes, seascapes); (iii) cultural heritage (authenticity of cultural heritage and sites); (iv) visitor accessibility (air, road); (v) existing service industry and product development (accommodations, restaurants, shops, guiding, information availability); (vi) existing tourism industry and site popularity; (vii) tourism funding opportunities for marketing and product development; (viii) double branding effect from the existence of parks and/or other type of protected area, and (ix) the designation effect—are used to examine the impacts of MPA designation (existing and proposed) in Northern Canada. The analysis suggests that the potential designation effect from four MPAs in Northern Canada is rather limited. Proponents and managers of MPAs would be best served to downplay the role of tourism and examine the role of other factors of local importance like livelihood during MPA designation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".