The United States 'Marine Mammal Protection Act' (MMPA): policy implications, challenges, opportunities and a strategy for the east coast sealing industry
Bibliographic record
Abstract
Since 1972 restrictions arising from the United States Marine Mammal Protection Act (MMPA) have seriously hampered efforts to develop commercial markets for seal products to the United States, and other international markets influenced by the MMPA. This has resulted in significant negative economic impacts on the east coast Canadian seal fishery. The exemption of harp seal products from the restrictive grip of the MMPA is critical if the United States market for seal products is to be effectively developed. It is anticipated that access to the United States market would present new market opportunities for the sealing industry, particularly in the fur and neutraceutical sectors. The U.S. 'health food' sector alone is estimated to be worth $80 billion per year (Ho, 2003). This market includes all omega-three, oil based concentrates and a full range of herbal and homeopathic remedies purchased through the health food system. -- There is limited historical United States market data for seal products, as seal products impacted by 'MMPA' have not been exported to that market since the early 1970's. Therefore, to evaluate market potential, this report establishes linkages using similar products in the categories of other furs and marine oils. The potential American market for fashion fur items is significant, with United States fur sales of $1.53 billion reported in 2002. (http://www.nafa.ca/page.asp.) These strong market indicators might present an opportunity for the Canadian sealing industry, should the American market become accessible. -- The economic benefits of the current seal fishery and the total value of the seal industry to Newfoundland and Labrador is an important factor in determining our export readiness for developing the United States markets. In 2007, the sealing industry in Newfoundland and Labrador employed 6,000 harvesters and over 300 production employees and there are spin off benefits in service industries, supplies, transportation, vessel and plant maintenance. (T. Grace, personal communication, June, 2008). -- This paper analyzes important issues surrounding the MMPA, including the more recent activities related to the Act. The recommendations in this paper focus on a strategy for Governments, the private sector and trade associations, to address issues related to MMPA. These recommendations will be of particular interest to the Department of Fisheries and Aquaculture and the Canadian Sealers Association, who have coordinated their efforts since 2004 in support of amendments to the MMPA.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.019 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".