Working Better Together: Fisheries and Tourism in Newfoundland and Labrador
Bibliographic record
Abstract
In Newfoundland and Labrador, as elsewhere, there is a strong but poorly acknowledged and poorly documented interdependence between the fisheries and tourism sectors. This interdependence is particularly strong in rural areas, including in fishing communities, where much of Newfoundland and Labrador’s tourism happens. Tourists want to consume local seafood, experience fisheries and fishing culture, and to meet people engaged in and knowledgeable about fisheries and the marine environment; local fisheries provide much of the seafood tourists eat, some members of fishing families work in the tourism sector, and fishing families are among the clientele who patronize local restaurants and hotels. Our rural communities are experiencing high rates of outmigration and rural populations are aging. The resources available to support economic development in rural areas, including in fisheries and tourism, are declining (as exemplified by recent cuts to Parks Canada and to support for the Regional Economic Development Boards). Employment in both fisheries and tourism is highly seasonal (particularly in rural areas) and some employers are finding it difficult to find appropriately skilled, local workers. Aging labour forces mean this challenge is likely to increase in the future. Both sectors are also very vulnerable to changes in global markets and to environmental and other changes. Unfortunately, from a policy and organizational perspective, Newfoundland and Labrador’s commercial fisheries and tourism industries have developed largely in isolation from each other. There has been no systematic effort to establish and promote synergies between the two sectors. As a result, there are potentially important missed opportunities for economic development that have the potential to create new business opportunities, strengthen existing businesses in both sectors, and to enhance the sustainability of both sectors as well as some rural communities and regions. On June 15, 2012, with support from the Rural Secretariat, the Community-University Research for Recovery Alliance at Memorial University (CURRA), the Harris Centre and the Newfoundland and Labrador Regional Economic Development Association (NLREDA) organized a multi-stakeholder workshop in St. John’s entitled Working Better Together: Fisheries and Tourism in Newfoundland and Labrador (See Appendix A for full program). The workshop used research done by the CURRA and insights from a multi-stakeholder panel to set the stage for facilitated small group discussions led by Ted Lomond from NLREDA and using the staff and facilitated discussion technology and expertise of the Rural Secretariat. Excellent support with organization and with the registration process was provided by Johan Joensen of the Harris Centre.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".