The splash and the ripples : assessing sustainability transition experiments
Bibliographic record
Abstract
Fostering sustainability transition is a pressing global societal challenge. Rising GHG emissions, severe social and economic inequality, unsustainable production and consumption activities, and global environmental degradation and its human consequences are all indicators of an unsustainable world. Sustainability Transition Experiments (STEs) have been proposed as a method to accelerate sustainability transitions in response to these challenges. We need methods by which to evaluate STEs, to understand what is happening within transition processes, and to provide insight to STE designers and facilitators as to the efficacy of their processes. This need led to the development of my research questions: How can we conceptualize, and evaluate, the contributions to sustainability transitions of STEs? Was the Energy Futures Lab effective in supporting sustainability transitions in Alberta? I develop a three-part evaluation framework of process, societal effects, and sustainability transition impacts leveraging a development pathway framing. I apply this framework to the case of the Energy Futures Lab in Alberta, Canada which has the goal of fostering transition to a sustainable energy system. Through participant interviews, document reviews, then coding all data in Nvivo, I assess the EFL and the evaluation framework itself. I conclude the framework to be an effective method with which to evaluate STEs. The most significant contribution of the framework is in conceptualizing and assessing sustainability transition impacts. By leveraging the framing of development pathways, this framework provides a detailed set of evaluation categories and indicators that can be used to assess development pathway change and the contribution of an STE to such change. The EFL itself strongly demonstrated commitment to process elements such as fairness, inclusivity, and transparency in its process design and implementation and included many elements that support transition. The EFL has demonstrable societal effects across all categories including individual capacity development, enhanced networks, and institutional change in policy and organizations. There are also examples where the EFL might have influenced characteristics of development pathway change. The evidence introduced in this dissertation leads me to conclude that, despite the limited evidence of direct impact, markers of transition are present illustrating the EFL’s contribution to sustainability transition impacts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".