The application of the collective impact initiative model for effective public consultation in Bonne Bay: example - ocean conservation
Bibliographic record
Abstract
Marine and coastal environments are not only crucial to the stability of the oceans' ecosystem but also to the socio-cultural, ecological, and economic well-being of their communities. The involvement of communities is, therefore, considered essential to generate innovative public policy to enhance the efficiency and long-lasting impact of the decision-making process. The Collective Impact Initiative (CII) model provides a novel framework to ensure cross-sector collaboration and effective public participation is in place to support such complex decision-making process. This thesis adopted the hypothetical case example of Marine Protected Area (MPA) planning for Bonne Bay in Gros Morne National Park as a hypothetical example to help evaluate the merits of CII application in support of natural resource planning and conservation in the region. Focus groups, interviews, and surveys were used to gather information from regional stakeholders. Through the information gathered, it was determined that the CII model holds great potential for the area both in terms of addressing community engagement challenges and providing a more effective structure for engagement in natural resource conservation.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".