The Influence of Mental Models on Decision-Making Around Social and Environmental Aspects: Insights from Ontario SMEs
Bibliographic record
Abstract
This study explores the influence of mental models on decision-‐making around social and environmental aspects within Ontario SMEs. The research covers areas of design, foresight and innovation as well as sustainability and strongly sustainable business models. Through exploring how business leaders consider and prepare for the future, this project engages foresight and innovation. Business leaders use mental-‐models to reach decisions by trying to imagine the possibilities compatible with what they know or believe (Johnson-‐Laird, 2012). \nOntario SMEs are a significant employment and economic contributor in Canada (Industry Canada, 2012). Progress towards greater sustainability by Ontario SME leaders would have a significant impact on the resiliency of our communities and the sustainability of our economy. While business leaders have access to information on social and environmental implications of their work, these items compete with others for priority and businesses continue to face a number of barriers to transitioning towards greater sustainability. The Design Probe method has been used to collect primary data from thirteen decision-‐makers within Ontario SMEs. The insights collected will serve managers who are interested in transitioning their businesses towards more sustainable behaviours by understanding common biases and errors as well as potential blindspots in their decisions. These insights are also useful for policy makers, NGOs and social entrepreneurs looking at accelerating the sustainability and resiliency of SMEs in Ontario and beyond. In addition, this research will inform the design brief of a range of tools such as those being developed by the Strongly Sustainable Business Model group (SSBMG) with insights into the most appropriate designs to support a shift towards strong sustainability. Further research can then identify how it may be possible to bring more SMEs to the level of sustainability leaders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".