Theorizing how the Three Horizons approach supports transformative learning: insights from advancing climate action in a Canadian Biosphere Reserve
Bibliographic record
Abstract
For society to make progress on sustainability requires businesses, alongside governments and non-government organizations, to take ambitious actions. Engaging small- and medium-sized enterprises (SMEs) is crucial in this context, as they represent one of the most common organizations in many economies and collectively contribute significantly to greenhouse gas emissions. In response, this research investigates how the Three Horizons approach (THA) can support SMEs through transformative learning to explore opportunities for climate actions in the Mont-Saint-Hilaire Biosphere Reserve (Canada). Using interviews and workshops, we examine the extent to which the THA leads to changes in assumptions and perspectives among SME owners. Our results demonstrate that in each horizon, participants went through transformative learning phases in a sequential order, i.e., developing assumptions based on experiences followed by challenging perspectives and transformation of perspectives. Furthermore, employing the THA (1) enabled participants to make sense of challenging situations, (2) generated experiences that helped participants to question established perspectives, and (3) created an innovation space conducive to producing action-oriented knowledge. Building on these findings, we theorize how the THA supports transformative learning processes and create conditions conducive for sustainability transformations.
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 | 0.001 | 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 | 0.000 |
| 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".