Assessing Wind Energy Acceptance Amongst Landowning Farmers in Alberta, Canada
Bibliographic record
Abstract
This study explores the acceptance of wind energy amongst rural landowning farmers in Alberta as they are a demographic that will be directly involved in and affected by wind energy development in the province. This thesis project uses data from an online survey completed by 401 Albertan landowning farmers between December 2018 and March 2019. The introductory chapter overviews the social acceptance of wind energy (SAWE) literature, the Albertan energy and wind energy landscape, the project background, and research methodology. In Chapter 2, I use ordered logistic regressions to assess how political ideology, fossil fuel preferences, and beliefs about wind energy impact attitudes towards wind energy (i.e., wind acceptance). I also explore whether wind energy is a politically polarized topic by looking for patterns in wind energy opinions across political divisions. The data suggests wind energy views are not politically polarized nor even polarized within this demographic as few expressed strong opinions for or against this type of energy development. Instead, Albertan landowners appear to take diverse, moderate, and fragmented positions on various aspects of wind energy, a finding that suggests they are open to amending their views. Beliefs about the economic and environmental impacts of wind energy appear crucial in shaping landowners’ overall stance on this low-carbon technology. Chapter 3 is an exploratory study investigating the relationship between different beliefs about climate change and wind energy acceptance. Binomial logistic regressions suggest believing in the efficacy of and feeling a sense of responsibility in climate action makes one less likely to oppose wind energy, although perceived social norms had a stronger impact. Additionally, I use an exploratory cluster analysis to identify two main climate beliefs profiles, which I name as the Climate Realists and Climate Skeptics. These analyses suggest climate denial beliefs are common within this demographic, with many expressing climate denial beliefs that are strong and therefore unlikely to be reformed. Chapter 4 synthesizes insights from the project as a whole and proposes avenues for further energy social science research in Alberta.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".