Exploring community engagement in tidal energy development: Issues and challenges
Bibliographic record
Abstract
Renewable energy development has become increasingly important in Canada over the last decade. The establishment of renewable energy targets to address greenhouse gas emissions has been the primary catalyst for this development. Canada has recently been recognized as a world leader in the production and use of renewable energy (Natural Resources Canada, 2014). Nova Scotia, like many other provinces, has set a renewable energy target. This increased reliance on renewable energy, and tidal energy, has called for the industry to practice community engagement. Community engagement though, has many issues and challenges to be considered. The purpose of this research is to better understand key issues and challenges in stakeholder and community engagement in relation to tidal energy development in Nova Scotia. Qualitative research in the form of in-depth, semi-structured interviews with Nova Scotia tidal energy industry professionals explored key issues in stakeholder and community engagement. Results provide insights into key issues in four thematic areas that include role of government, exploring community benefits, First Nations and stakeholders, and engagement principles and practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| 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".