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Record W2339914999 · doi:10.1080/08865655.2016.1165133

Informal Economies in European and American Cross-border Regions

2015· article· en· W2339914999 on OpenAlexvenueno aff
Harlan Koff

Bibliographic record

VenueJournal of Borderlands Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceEconomic geographyEconomyEconomicsInternational tradeEconomic system

Abstract

fetched live from OpenAlex

Informality is often linked to borderlands in both academic scholarship and political debates. On one hand, border regions are known for the flow of goods, services and labor and, of course, borders represent state attempts to control or regulate these flows. At the same time, scholars of border politics often discuss the weakness of state administrations in border regions where authorities are far from central governments. Despite the clear relevance of informal sectors for borderlands studies, there is a dearth of analysis of this topic in border areas, especially in comparative terms. This article presents a comparative cross-regional study of informality in European (the Eurométropole and Bari, Italy–Durres, Albania) and continental American (San-Diego, USA–Tijuana, Mexico and Cúcuta, Colombia–San Crístobal, Venezuela) cases. It responds to the following research questions: How can we compare informality in cross-border regions? How does informality relate to illegality in these regions? How can regional organizations respond to the social impacts of informality?

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0000.000
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 head0.048
GPT teacher head0.431
Teacher spread0.384 · 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 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

Citations24
Published2015
Admission routes1
Has abstractyes

Explore more

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