Testing the Waters of an Aquaculture Index of Well-Being
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social licence is rooted in perceptions of local rights holders and stakeholders. The disease focus of aquaculture health policy, practices, and research insufficiently reflects societal expectations for aquafarms to protect health of shared resources. Our case study of Atlantic salmon (Salmo salar) farming in British Columbia (BC), Canada, assessed the readiness of aquaculture to change from managing health as the absence of disease to a perspective of health as well-being to maintain social licence. We drafted an index of well-being based on agroecosystem health and socio-ecological health principles. We then reviewed publicly available industry and government information and undertook key informant interviews. The industry was well situated to develop and use a well-being index. Interviewees saw value in a well-being index and found it compatible with area-based management. Many elements of the index were being collected but there would be challenges to overcoming feelings of over-regulation; negotiating specific indicators for local situations; and securing the necessary expertise to integrate and assess the diversity of information. Health conflicts and disagreements facing salmon farming in BC are like those in other aquaculture sectors. Social licence may be improved if companies transparently report their state of the health by adapting this conceptual framework.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it