MétaCan
Menu
Back to cohort
Record W2514211761 · doi:10.1016/j.ijdrr.2016.08.006

Resilience in a complex world – Avoiding cross-sector collapse

2016· article· en· W2514211761 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Disaster Risk Reduction · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsWestern University
Fundersnot available
KeywordsInterdependenceResilience (materials science)Civil societyCrashProcess (computing)BusinessPsychological resilienceEconomic systemRisk analysis (engineering)EngineeringPolitical scienceIndustrial organizationEconomicsComputer science

Abstract

fetched live from OpenAlex

In a more and more globalized world we have created unprecedented connectivity, mainly by striving for better business opportunities. But with such a strong global connectivity, the risks associated have also changed: formerly local issues can now have global impact, and systems are often too complex to fully understand their interdependencies. In addition, the speed of change is increasing in many sectors of society and the economy. So we are building a future world with more and more interdependencies of which we understand less and less, and this process is accelerating sharply. This means that we are mixing together the typical ingredients for an upcoming crash, which in the worst case could mean the collapse of society as we know it. To avoid such a scenario, a coordinated effort of public authorities, civil society, industry, and academia will be required.

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.

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 score0.048
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.292
Teacher spread0.244 · 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