How mental models influence decision \n- \nmaking: \ninsights from leaders of sustainable Ontario SMEs
Bibliographic record
Abstract
This research project explored the relationship between mental models and core strategic decisions about sustainability, by focusing on leaders of Ontario Small to Medium sized Enterprises (SMEs) who were already recognized by third parties as leading in the transition towards sustainability. A design probe was used to collect a qualitative data and visual information from the participants’ own perspective. Twelve SME leaders completed the design probe, sharing the ir thoughts, values, past experiences and future plans through a series of generative prompts. The results led to five key insights about the participants’ mental models and decision making about sustainability, as well as implications for SME Strategy and the design of strategic tools for SME leaders. A model was proposed to help other SMEs envision ways to catalyze larger scale impacts beyond their own internal operational decisions. Overall, the five insights revealed the importance of thinking that is longterm, creative, global, and systems oriented, as well as the challenge of sharing mental models with internal and external stakeholders.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".