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.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".