Forms of Rationality and Uncertainty: Energy Efficiency in Ontario Hospitals
Bibliographic record
Abstract
This dissertation explores the dynamics surrounding energy efficiency practices in Ontario hospitals using a manuscript-based approach, and consisting of a chapter dedicated to a theoretical overview, and two separate but related studies with first-hand data. The theoretical overview reviews the diverse perspectives, approaches and theories used to understand energy behaviour at multiple levels of analysis: the individual, organizational and institutional level. The first study is based on a grounded theory approach to generate theory on how energy efficiency practices occur. The research explores how individuals and organizations make sense of their environment, the rationalities and implicit assumptions that shape their understandings, and the approaches they use to deal with uncertainty surrounding energy efficiency decision-making. Structural conditions are identified, frames for considering energy efficiency are uncovered, and two approaches for dealing with uncertainty are interpreted. For hospitals ‘Demanding Certainty’, management demands uncertainties be completely controlled. Energy efficiency is communicate by presenting, pitching and confirming to management, resulting in risk avoiding organizations where individuals absorb associated risks and energy efficiency stalls. For hospitals ‘Managing Complexity’ energy efficiency is complementary to patient care. Leadership is understanding of inherent risks and buy-in fosters communication through negotiation and collaboration, resulting in improved energy efficiency performance. The second study utilizes a quantitative approach to operationalize constructs developed in the first study to test theory and to drive further understandings. The results support the earlier findings and suggest that forms of rationality and dealing with uncertainty are intertwined, with both predicting energy efficiency performance. Applying these findings more broadly to climate policy can ensure new policies contextualize the risk-taking needed by both government and external stakeholders to drive not only innovation, but also the risk-taking needed to achieve their objectives. Finally, a conclusion summarizes the overall findings, presents limitations, and directions for future research.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".