Making collaboration work: an evaluation of marine protected area planning processes on Canada’s Pacific Coast
Bibliographic record
Abstract
It is widely agreed that marine protected areas (MPAs), which can provide long-term protection to marine ecosystems of high ecological, economic, social and cultural value, will only be successful if they are designed and implemented with the involvement and support of stakeholders and other key actors. Putting a collaborative approach into practice is not easy, though. Appropriate governance structures, which formalize and facilitate information sharing, consensus building, and decision making are necessary, but insufficient. Also needed is a shared interest on the part of all groups – beginning with MPA agencies themselves – to work together, notwithstanding the often considerable investments of time, effort and material resources that are required. Perhaps most fundamentally, effective collaboration depends on trust, and strong interpersonal relationships. Consistent with a global trend in favour of more inclusive and participatory approaches to protected area planning and management, Canada’s federal government has set out to develop a national system of MPAs in cooperation with a broad array of interest groups, including marine resource users and other stakeholders; government actors with responsibilities and authorities for oceans activities that relate to the objectives of MPAs; and Aboriginal communities and organizations within whose territories MPAs are situated. The overarching goal of the study was to understand the extent to which federal MPAs in British Columbia (BC), Canada, are established collaboratively, and what is required to overcome obstacles to successful collaboration. This goal was pursued through an in-depth investigation of two MPA planning processes in BC: the proposed Race Rocks MPA, at the southern tip of Vancouver Island; and the Gwaii Haanas National Marine Conservation Area Reserve and Haida Heritage Site, in the Haida Gwaii archipelago. Data for the study was collected through semi-structured interviews; documentary research; and a participant questionnaire. The study found that, while MPA agencies engaged with outside parties in a variety of ways to plan Race Rocks and Gwaii Haanas, these processes fell short of expectations for genuine collaboration in a number of respects. In the case of Race Rocks, this has resulted in the failure (for a second time) to designate the MPA. The dissertation illuminates the challenges and shortcomings that were encountered in both cases, and offers practical solutions to address them.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".