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Record W4386220326 · doi:10.3390/su151712950

Global Discontinuity: Time for a Paradigm Shift in Global Scenario Analysis

2023· article· en· W4386220326 on OpenAlexaff
Dale S. Rothman, Paul Raskin, Kasper Kok, John Robinson, Jill Jäger, Barry B. Hughes, Paul C. Sutton

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsParadigm shiftFutures contractEmbeddednessMainstreamNarrativeDiscontinuity (linguistics)Political sciencePositive economicsSociologyEconomicsEpistemologySocial scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.257
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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