From Fish to Folk Art: Creating a Heritage-Based Place Identity in Ferryland, Newfoundland and Labrador
Bibliographic record
Abstract
This study uncovers the social processes behind the transformation of underdeveloped spaces into landscapes of consumption. We focus on Ferryland, Newfoundland and Labrador, a community that is developing a heritage-based place identity in response to the collapse of the northern cod fishery. Ferryland's 'heritage-scape' place identity is first established. Responsible stakeholders and their motivations are revealed, and contestation accompanying the transformation exposed. The future of this identity is then considered in light of local plans and provincial directives. Using a variety of primary and secondary sources, we demonstrate that Ferryland is an emerging heritage-scape, whose creation has been facilitated largely by civic (non-profit) and public sector co-operation. We find limited evidence of contestation, which we attribute to widespread support for the tourist industry, recognition of its economic benefits, and retention of the original rural landscape identity. We acknowledge that civic sector strategic planning, coupled with the province's mandate for growth, may stimulate future tourism development. This scenario will only unfold, however, in the presence of clear direction, a willing workforce, entrepreneurial initiatives and additional funding. Keywords: heritage, rural landscape identity, underdevelopment, tourism
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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 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".