Surviving Meltdowns That Cannot Be Prevented: Review of Gaps in Managing Uncertainty and Addressing Existential Vulnerabilities
Bibliographic record
Abstract
We make all decisions in the context of what we know and can envision. However, catastrophes often arise from what we had not known or had not envisioned previously. Approaches that work for addressing what can be envisioned are not useful in preventing catastrophic meltdowns arising from what cannot be envisioned ex ante. In extreme situations, such meltdowns can represent existential exposure to an organization, and thus cannot be ignored. Despite advances in risk management, a gap in addressing what cannot be envisioned ex ante has existed since Frank Knight’s designation of risk and uncertainty in 1921. As a result, organizations continue to employ approaches that may be ineffective against catastrophic meltdowns from the unknown. There is an urgent need to address this gap by scholars because as our world becomes more complex and globally interconnected our organizations and systems become increasingly vulnerable to this exposure from the unknown. The need and the urgency to address this exposure will only increase as, in addition to everyday operations, climate change represents a unique and growing challenge because everything about it, and its impact at organization levels, is unknown and beyond what can be envisioned today. Its impact, if it materializes, will be global and widespread, and it is likely that no organization and system will escape it. Thus, a mechanism to address the unknown that cannot be envisioned should be a priority for scholars and for organizations. There are clear differences between exposure from what can be envisioned and exposure arising from the unknown that require addressing the unknown differently. This paper explores these differences and then offers an approach to address the exposure from the unknown in practice through a disciplined managerial process to ensure that organizations can survive meltdowns that cannot be prevented.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".