Mobilizing values: using perceptions of barachois ponds in Nova Scotia to advance informed management
Bibliographic record
Abstract
Meaningful engagement is increasingly used as a management tool for understanding the multitude of complex values and potential conflicts around marine conservation and the production of conservation strategies deemed acceptable by local communities. Barachois ponds, akin to coastal lagoons, are recognized coastal wetlands in Nova Scotia, Canada, given their distinct ecosystem services, including provisioning, regulating, and cultural services. This study examines the current discourses around the management of barachois ponds and how an increased understanding of these perceptions held by stakeholders and managers might be used to better inform integrated management of these wetland ecosystems. A mixed-methods research design using Q methodology was employed to acquire data on social perceptions surrounding the management of barachois ponds fringing the Bras d’Or Lake in Cape Breton, Nova Scotia, Canada. Four dominant perspectives were identified: the leave-them-be conservationists, the sustainable developers, the management reformists, and the science-based conservationists. Six key issue themes emerged based on an in-depth examination of these different perspectives and Q sort data. This study found that an increased awareness of the ecological, social, and cultural values attributed to barachois ponds by key stakeholders could play a critical role in better informing wetland management decision-making in Nova Scotia and elsewhere.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".