Eating the ocean seafood and consumer culture in Canada
Bibliographic record
Abstract
"During the first half of the twentieth century, Canadian fisheries regularly produced more fish than markets could absorb, driving down profits and wages. To address this, both industry and government sought to stimulate domestic consumption via increased advertising. In Eating the Ocean Brian Payne explores how government-funded marketing called upon Canadian housewives to prepare more seafood meals to improve family health and aid an industry central to Canadian identity and heritage. The goal was first to make seafood a central element of a "wholesome" diet as a solution to a perceived nutritional crisis, and, second, to aid industry recovery and growth while decreasing Canadian fisheries’ dependency on foreign markets. But fishery managers and policymakers fundamentally miscalculated consumer demand, wrongly assuming that Canadians could and would eat more seafood. Fisheries continued to extract more fish than the environment and the market could sustain, and the collapse of the nation’s fisheries that we are now seeing has as much to do with failed assessments of market demand as it does with faulty extraction practices. Using internal communications between industry leaders and Ottawa bureaucrats, as well as advertising and promotional material published in the nation’s leading magazines, national and local newspapers, and radio programming, Eating the Ocean traces the flawed understanding of not only supply but demand, a misguided gamble that caused fisheries to become the most mismanaged resource economy in early-twentieth-century Canada."--
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".