Environmental Governance and The Oil-Tourism Interface
Bibliographic record
Abstract
Nature-based tourism and oil development often share social and ecological space, but environmental governance rarely brings the two sectors into contact through planning or policy-making. However, this “connective” dimension of governance is vital for thinking about how coastal societies navigate relationships across different forms of development. By comparing Denmark, Iceland, Newfoundland and Labrador, Norway, and Scotland, we examine how political engagement happens within and across nature-based tourism and offshore oil. Nature-based tourism promotes the experience of natural environments and encounters with wildlife, including whales, seals, or seabirds, but often involves fossil fuel intensive travel. Offshore oil promises economic benefits from employment and royalty payments, but is a form of fossil fuel-intensive resource extraction. Political engagement across these sectors is limited. It occurs most often when there is conflict over extending oil exploration and extraction into new regions. Lack of attention to the connective dimension of environmental governance is explained by several related factors. First, there are significant differences in environmental governance across the two sectors. Tourism governance is oriented around local/regional politics, and involves a more diffuse network of actors, while offshore oil governance is oriented around regional/national/international politics, and involves a more concentrated network of actors. Second, in most cases, offshore oil governance is better established and developed than tourism governance. Third, the separation of oil and tourism within environmental governance reflects the greater perceived importance of offshore oil to the economies and social imaginaries of host communities. By contrast, where tourism is more central to the political economy and social imaginary of host communities there is more engagement across the sectors.
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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 | 0.002 |
| 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 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".