Cognitive Processes for Turning Social and Environmental Problems into Positive Solutions
Bibliographic record
Abstract
Businesses have the potential to provide economic solutions to some of world’s most pressing social and environmental problems. Yet identifying positive solutions from otherwise dire circumstances requires individuals to escape the negative frame of such problems. In this paper, we conduct a verbal protocol study with 24 experienced sustainable entrepreneurs to investigate the reasoning strategies they mobilize when facing social or ecological issues, and examine the extent to which re-framing facilitates their identification of creative solution ideas. From a research standpoint, our study contributes new insights into the nature of reframing. More specifically, the results indicate that reframing proceeds from a cascade of cognitive processes that include frame breaking, representational changes, and new frame constructing. As such, our study casts light on the cognitive dynamics that underpin individual and organizational efforts to reframe problems into solutions, providing empirical evidence that reframing is a relevant cognitive feat of managerial thinking when addressing the grand societal challenges of our time.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".