Overcoming Divisive Strategic Environmental Assessments for Offshore Oil and Gas in Nova Scotia, Canada
Bibliographic record
Abstract
In Nova Scotia, strategic environmental assessments (SEAs) are used to scope the potential impacts of offshore oil and gas activities in the early stages of regulatory decision-making. This study examined stakeholder perceptions and involvement in SEAs for offshore oil and gas decisions on areas being opened by the provincial government for development. Stakeholder comments from 12 SEAs (2003–2019) were evaluated, and 25 interviews with strategic actors involved in the assessments were undertaken and coded. The results reveal actors in Nova Scotia are divided over the effectiveness of a sector-specific SEA: while federal–provincial governments and the regulator were satisfied with SEA function, non-governmental stakeholders questioned the credibility of the regulator as well as the intent and utility of SEAs. Policy recommendations are outlined to remedy gaps in SEA processes, notably implementing integrated management via marine spatial planning in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".