Implications of complexity and chaos theories for organizations that learn
Bibliographic record
Abstract
In 1996 Hubert Saint‐Onge and Smith published an article (“The evolutionary organization: avoiding a Titanic fate”, in The Learning Organization , Vol. 3 No. 4), based on their experience at the Canadian Imperial Bank of Commerce (CIBC). It was established at CIBC that change could be successfully facilitated through blended application of theory such as system dynamics, and the then emerging notions of “chaos and complexity”. The resulting enterprise was termed an evolutionary organization (EVO), and CIBC has continued since to re‐invent itself with great success. Although the all‐embracing nature of chaos and complexity was understood, in retrospect the impact of non‐rational people‐factors, e.g. emotion, trust, openness, spirituality were underestimated. Introduces the six papers included in this special issue, which illustrate how much more sophisticated chaos and complexity have become in the decade since Hubert Saint‐Onge and Smith first began to apply the notions at CIBC. However, although the papers in this issue present some evidence of managerial “take‐up” of chaos and complexity, whether “take‐off” will ever ensue is questionable. It is proposed that, just as in the 1990s, if there is one thing that more than any other stands in the way of exploration and adoption of these ideas, it is management mindsets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".