Taco Tuesday Anyone? Understanding student demand and knowledge of local seafood.
Bibliographic record
Abstract
The Gulf of Maine fishing industry continues to be a major economic driver throughout the region, integrating culture, history, and development across working waterfronts spanning thousands of miles from Cape Cod Massachusetts in the south to Nova Scotia Canada in the north. Local seafood harvesting and consumption attract visitors from around the world to enjoy the abundance of lobster, clams, mussels and oysters from the Gulf of Maine. What tourists and residents alike may not understand is the opportunity of other species that are plentiful, economical and delicious. Coupled with the local food movement, underutilized seafood presents additional potential especially within the environmental-conscious consumer groups. Thus, the purpose of this study was to evaluate seafood consumption, species preferences, and eco-label knowledge within one such consumer setting (higher education, college campus setting). College students from the University of Southern Maine were surveyed in the fall of 2017 (N=227) and spring of 2018 (N=320). Most consume seafood regularly, with more than half of participants eating fish or shellfish weekly or monthly. Top species preferences were salmon, shrimp and tuna, followed by local New England fare, ending with underutilized fish species being the least popular. Recognition of seafood eco-labeling trended positively, yet reading of educational outreach was poor despite strong desire for sustainable, local, healthy food. Study participants viewed cost as the top barrier for consuming local seafood. When offered at a price equivalent, lack of visibility was the top impediment for purchasing the underutilized fish entrée. To summarize, this study demonstrates strong demand for regionally and responsibly harvested seafood while highlighting the need for improved communication to market such seafood.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".