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Record W3169613051 · doi:10.18357/bigr22202120051

Unsustainable Borders: Globalization in a Climate-Disrupted World

2021· article· en· W3169613051 on OpenAlexaffvenue
Simon Dalby

Bibliographic record

VenueBorders in Globalization Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsBalsillie School of International Affairs
Fundersnot available
KeywordsGlobalizationAnthropoceneContextualizationPoliticsEnvironmental ethicsContext (archaeology)SustainabilityPolitical scienceClimate changePolitical economyGlobal warmingSociologySocial scienceGeographyLawEcology

Abstract

fetched live from OpenAlex

Climate change and the responses to it reveal starkly different assumptions about borders, security and the ethical communities for whom politicians and activists speak. Starting with the contrasting perspectives of international activist Greta Thunberg and United States President Donald Trump on climate change this essay highlights the diverse political assumptions implicit in debates about contemporary globalization. Rapidly rising greenhouse gas emissions and increasingly severe climate change impacts and accelerating extinctions are the new context for scholarly work in the Anthropocene. Incorporating insights from earth system sciences and the emerging perspectives of planetary politics suggests a novel contextualization for contemporary social science which now needs to take non-stationarity and mobility as the appropriate context for investigating contemporary transformations. The challenge for social scientists and borders scholars is to think through how to link politics, ethics and bordering practices in ways that facilitate sustainability, while taking seriously the urgency of dealing with the rapidly changing material context that globalization has wrought.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.011
Scholarly communication0.0070.010
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.383
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations17
Published2021
Admission routes2
Has abstractyes

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