Underwater community gardens? Exploring community-based marine aquaculture as a coastal resource management strategy in Nova Scotia, Canada [graduate project].
Bibliographic record
Abstract
Aquaculture is one of the world’s fastest growing food production sectors and presents an opportunity for rural, coastal community development that can support livelihoods. An ecosystem approach to aquaculture (EAA) has been recommended to facilitate socially and environmentally sustainable development, yet there remains a need to better involve people in planning and operational aspects. Community-based management presents a possible option to advance an EAA in this way; however, context-specific research is needed to understand its potential application and suitability. This research explores community-based marine aquaculture (CBMA) in Nova Scotia (NS), Canada, on provincial and local-scales, using a mixed methods approach, which includes stakeholder interviews, geographic information system (GIS) analysis, and surveys, to examine its suitability as a coastal resource management strategy. Findings suggest that CBMA is a feasible approach to future aquaculture development in NS and its possible implementation is conceptualized. This research also initiates pilot methods that can be used to determine suitability of coastal communities, which factor the importance of community perceptions into the planning and operationalization process. Although this research was undertaken in the context of NS, it has implications that can help to further opportunities for CBMA in other regions of the world, supporting the advancement of the EAA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".