Climate: A changing environment for business
Bibliographic record
Abstract
Even as the world is becoming accustomed to the reality of climate change, its impacts are experienced by organizations in the form of more and increasingly disastrous events like storms, droughts and floods, as well as slower changes like sea level rise or changing growing seasons. Unprecedented losses, coupled with equally unprecedented levels of uncertainty around future losses, are already generating major challenges for organizational decision makers. Core to these climatic changes has been the way in which industrial and economic systems have treated the Earth’s natural systems – referred to in the scientific literature simply as “business as usual”. We argue that there is an urgent need to develop the theoretical foundations that can to help firms prepare for surprise (King, 1994), that is, to prepare for the difficult to predict and severe changes which are expected to affect organizational environments. Toward this foundation, we introduce and define “Massive Discontinuous Change” (MDC) as a new and critical construct at the core of building a better understanding of organizational and strategic implications. We then examine applicable management theories for their contributions to inform business strategy in the face of massive discontinuous change, focusing specifically on work in sustainability management, risk management and organizational change. We argue that more of the same equates to business as ususal. The paper closes with suggestions for future directions for management research on massive discontinuous change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".