Integrate and embrace or isolate and hide? Using Q-method to understand how to incorporate large-scale solar in rural Nova Scotia, Canada
Bibliographic record
Abstract
Large-scale solar (LSS) facilities, also called solar farms, are increasingly being built as part of the national transition to renewable energy. LSS can have landscape impacts that concern rural residents and cause community pushback. This paper identifies rural residents' views about integrating LSS into rural landscapes of Nova Scotia, Canada, a province new to LSS where community pushback has already occurred. We do not focus on whether LSS should happen, but how it should happen. Employing Q methodology with semi-structured interviews, 18 rural residents expressed their views by ranking 40 statements related to landscape impacts of LSS. Two distinct views emerged: LSS should either be (1) integrated and embraced in, or (2) isolated and hidden from, everyday rural landscapes. Strong consensus was identified around mitigating harm to local natural environments. The two views inhabit opposite poles of some common debates in landscape transition, including in environmental social science: landscape-technology fit versus misfit, climax versus non-equilibrium thinking, and land sparing versus sharing. We examine the implications of the one point of consensus, two views and three axes of landscape debate for large-scale solar development and associated public engagement processes. Practical contributions include prompts and cautions for discussions about LSS with the potentially affected local community, research domains to deepen understanding of local perspectives and increase technical options, and insight to inform LSS designers and landscape architects seeking to advance sustainable LSS development. • We ask how, not whether, to incorporate large-scale solar (LSS) in rural areas. • Q-method in three towns with different experiences with LSS generated two viewpoints. • The Integrate & Embrace view thinks climate change is a bigger risk than LSS. • The Isolate & Hide view favors single-use LSS set far from everyday rural landscapes. • Planning for LSS should consider landscape fit, stability, and multifunctionality.
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 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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".