Exploring the Future at the Edge of Chaos \n \nA transformation of Jackpine using strategic foresight
Bibliographic record
Abstract
This study examines the application and transformational impact of strategic foresight on a small business, specifically examining Jackpine, a design and strategy firm. \n \nThe research was motivated by a desire to enhance Jackpine’s business model and operational resilience through structured foresight integration. The investigation involved a series of workshops and the implementation of the Foresight Maturity Model (FMM), assessing changes in foresight capacity over time (Grim, 2009). \n \nInitial results indicate that Jackpine experienced a significant enhancement in foresight capabilities, with an average improvement of approximately 32% across various foresight disciplines. This improvement was particularly notable in visioning, planning, and scanning, where efforts shifted from ad hoc to mature levels of foresight execution. \n \nThe study utilized participatory methods, engaging both internal team members and clients in foresight exercises, which facilitated practical applications and insights. The findings underscore the utility of strategic foresight in fostering a proactive, rather than reactive, organizational culture (Conway, 2019). This aligns with Stuart Kauffman’s "edge of chaos" theory, which posits that the most innovative and adaptive states occur at the boundary between order and chaos (Kauffman, 1993). \n \nBy situating Jackpine at this juncture, the firm has cultivated an environment where strategic foresight drives innovation and adaptability. \n \nThis research contributes to the understanding of how small businesses can effectively implement and benefit from foresight practices, providing a model for others in the industry. Future work could explore the long-term impacts of sustained foresight practices on business resilience and innovation capacity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.002 |
| 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".