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Record W2811419971 · doi:10.1080/08865655.2016.1197789

Cashable Value: Social Capital and Practical Habits in the Analysis of Collaborative Cross-Border Economic Development

2018· article· en· W2811419971 on OpenAlexvenueno aff
Seth Pipkin

Bibliographic record

VenueJournal of Borderlands Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsSocial capitalValue (mathematics)Capital (architecture)Economic systemEconomicsClassical economicsSociologyComputer scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

Today’s climate of heightened border security has intensified a key challenge found on border regions: coordination across institutional regimes. For 30 years or more a popular concept for explaining groups’ abilities to handle such challenges has been social capital and its variants, such as collective efficacy. While in many respects useful, these concepts are hindered in their explanatory power due to the lack of precision in their definition and a misplaced analogy of group capacities to capital stocks. This paper therefore takes a dissenting view from the special issue’s premise that questions of cross-border collaboration are fully amenable to a social capital-based framework. Rather, it aims to contribute to the borderlands studies research community’s tools for moving beyond some widely-acknowledged limitations to social capital frameworks by introducing complementary concepts and methods that emerge from an historical ethnography of two US-Mexico border city pairs whose economic fortunes diverged after the enactment of the North American Free Trade Agreement (NAFTA). Although capital stocks cannot account for this divergence, local practices and habits of business and political elites’ policy implementation do, suggesting that researchers interested in these topics need to broaden their methods and concepts to help deal with the contradictory challenges of liberalized commerce and heightened security that today’s border regions face.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.034
GPT teacher head0.434
Teacher spread0.400 · 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 designQualitative
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

Citations4
Published2018
Admission routes1
Has abstractyes

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