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Imagining and Imaging Borders: Understanding Borderlands for Global Sustainability

2013· article· en· W2138654260 on OpenAlexaff
Victor Konrad

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsCarleton University
Fundersnot available
KeywordsSustainabilityCorporate governanceGlobalizationPoliticsPolitical sciencePresentation (obstetrics)Global governanceHuman securityPolitical economyInternational relationsSociologyEconomicsLawManagement

Abstract

fetched live from OpenAlex

Abstract. Borders are increasingly complex human responses and social constructions in a world where globalizing forces confront basic human concerns for security and certainty. In an effort to provide a background to assess research directions for imaging borders, this paper explores what we know about borders, and what we do not know well about borders. Borders in globalization are the meeting points of globalizing forces of security, trade and migration flows with emerging technologies, self determination and regionalization around the world. We need to know more about how: self determination fuels secessions and new borders; borders result from complex rather than simple policy and governance issues; borders depend on the political clout of borderland communities; market and migration flows impact borders; and borders are always in motion. The paper shows how these organizing principles underlie the basic themes of border governance, flows, culture, history, security and sustainability. Finally, the paper offers two brief illustrations of border imaging to link this presentation to the following discussion of the workshop.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations1
Published2013
Admission routes1
Has abstractyes

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