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Record W4386888490 · doi:10.5751/es-14351-280319

Transboundary flows in the metacoupled Anthropocene: typology, methods, and governance for global sustainability

2023· article· en· W4386888490 on OpenAlexvenueno aff
Yingjie Li, Nan Jia, Xiang Yu, Nicholas Manning, Xin Lan, Jianguo Liu

Bibliographic record

VenueEcology and Society · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityMichigan State UniversityNorges Teknisk-Naturvitenskapelige UniversitetNational Science Foundation
KeywordsTypologySustainabilityAnthropoceneCorporate governanceResilience (materials science)PortfolioEnvironmental resource managementEarth system scienceMode (computer interface)BusinessEnvironmental planningEnvironmental scienceComputer scienceGeographyGeologyEcology

Abstract

fetched live from OpenAlex

The world has become increasingly metacoupled through flows of materials, energy, people, capital, and information within and across systems. Transboundary flows, connecting adjacent and distant systems, are deemed the most critical indicators for measuring the intensity of interactions among coupled human-natural systems. To advance metacoupling flow research and governance, we make the first attempt to develop a typology of transboundary flows using six flow attributes (i.e., type, magnitude, direction, distance, time, and mode). Furthermore, we synthesize a portfolio of quantitative and practical methods for characterizing transboundary flows. To effectively govern transboundary flows for global sustainability and resilience, we highlight the need to recognize the shared risks and goals embedded in the interlinkages, use system thinking, and enhance multilateral cooperation.

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)0.002
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 score0.038
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.328
Teacher spread0.318 · 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

Citations32
Published2023
Admission routes1
Has abstractyes

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