Global Discontinuity: Time for a Paradigm Shift in Global Scenario Analysis
Bibliographic record
Abstract
The evolutionary paths of social-ecological systems comprise periods of structural continuity punctuated by moments of convulsive change. Various forms of systemic global shock could materialize in the coming decades, triggered by the climate crisis, social disruption, economic breakdown, financial collapse, nuclear conflict, or pandemics. The unfolding COVID-19 pandemic stands as a real-time example of an interruption of historic continuity. More hopefully, deep institutional and cultural shifts could rapidly usher in more resilient forms of global civilization. These plausible possibilities challenge scenario studies to spotlight discontinuous futures, an imperative that has not been adequately met. Several factors—for example, gradualist theories of change, scientific reticence, the lure of quantitative tractability, embeddedness in policymaking processes—have rendered mainstream scenario analysis ill-suited to the task. The emphasis on continuity fails to alert decision makers and the public to the risks and opportunities latent in our singular historical moment. A shift to a paradigm that foregrounds discontinuity is long overdue, calling for efforts to broaden the base of persons involved; devote more attention to balancing narrative storytelling and a broader range of quantitative methods; and apply and develop methods to explicitly consider discontinuities in global scenario development.
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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 | 0.062 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.016 | 0.041 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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".