Establishing an agenda for social studies research in marine renewable energy
Bibliographic record
Abstract
To date, academic research relating to Marine Renewable Energy (MRE) has largely focused on resource assessment, technical viability and environmental impact. Experiences from onshore renewable energy tell us that social acceptability is equally critical to project success. However, the specific nature of the marine environment, patterns of resource distribution and governance means experiences from onshore may not be directly applicable to MRE and the marine environment. This paper sets out an agenda for social studies research linked to MRE, identifying key topics for future research: (i) economic impacts; (ii) wealth distribution and community benefits; (iii) communication and knowledge flow; (iv) consultation processes; (v) dealing with uncertainty; (vi) public attitudes; and (vii) planning processes. This agenda is based on the findings of the first workshop of ISSMER, an international research network of social scientists with interests in marine renewable energy. Importantly, this research agenda has been informed by the experiences of developers, regulators and community groups in Orkney. The Orkney archipelago, off the north coast of Scotland, is home to the most intense cluster of MRE research, development and deployment activity in the world today.
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 | 0.199 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.022 | 0.063 |
| Scholarly communication | 0.049 | 0.054 |
| Open science | 0.007 | 0.039 |
| Research integrity | 0.040 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".