Drivers of social acceptability for bivalve aquaculture in Atlantic Canadian communities
Bibliographic record
Abstract
Aquaculture is a growing sector because of increased global demands for seafood; bivalve aquaculture production is also increasing in specific regions because of its perceived sustainability and similar environmental interactions across ecosystems. As socioeconomic impacts on prospective sites may differ, this research aimed to perform a high-level scoping of environmental, social, and economic drivers informing social acceptability of bivalve aquaculture in two communities in Nova Scotia and Prince Edward Island, Canada. Communities were surveyed through online questionnaires designed to examine bivalve farming perceptions, information sources, and potential deviations between communities. Results suggested that community perceptions of environmental effects were both positive and negative, social effects were mostly negative, and economic effects were somewhat positive. Results further suggested that insufficient transparency regarding industry practices and the local communication network may have a role in shaping bivalve farming perceptions. Variation between communities regarding perceived social and economic drivers of social acceptability emphasized the importance of community-based research to understand emerging and existing conflicts, including the role information sources may have in driving acceptability. Accordingly, aquaculture regulators and managers should consider community socioeconomic priorities and improved transparency about industry practices when evaluating prospective sites.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 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".