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Record W2796220712 · doi:10.1177/0308518x18768286

(Re-)writing markets: Law and contested payment geographies

2018· article· en· W2796220712 on OpenAlexfundno aff
Shaina Potts

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
FundersConcordia University
KeywordsCreditorPaymentState (computer science)GlobalizationStructuringInterpretation (philosophy)Variety (cybernetics)PoliticsLawLaw and economicsPolitical economySociologyEconomicsBusinessPolitical scienceFinanceDebt

Abstract

fetched live from OpenAlex

While many emphasize the supposed frictionlessness and instantaneity of global financial flows, economic geographers have done important work placing globalization in concrete practices and spaces. Yet, cross-border payment transactions, which are constitutive of transnational markets, remain understudied. In this paper, I use creditor litigation against Argentina as a lens through which to explore material geographies of transnational financial payments. This litigation sheds light on the fundamental role of law (especially US common law) in structuring most major payment transactions today. Payment “flows” are not continuous at all, but rather legally divided into discrete spatial segments—and remapping these divisions, via litigation, has become a focal point of struggle between creditors and debtors, as well as among financiers. Fierce debates over contracts and their interpretation have been central in these battles. Furthermore, these financial geographies remain inextricably entangled not only with business actors, but with legal and political actors as well—law anchors economic geographies in state spaces and (often contradictory) state interests at a variety of scales.

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)0.009
metaresearch head score (Gemma)0.030
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: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.020
Scholarly communication0.0130.014
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.192
Teacher spread0.176 · 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
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

Citations38
Published2018
Admission routes1
Has abstractyes

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