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Record W3117712207 · doi:10.1080/08865655.2020.1855227

Cross-Border Integration, Cooperation and Governance: A Systems Approach for Evaluating “Good” Governance in Cross-Border Regions

2020· article· en· W3117712207 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Borderlands Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceCross-border cooperationMulti-level governanceEconomic systemRegional scienceKey (lock)Political scienceBusinessEconomic geographyEconomicsGeographyComputer science

Abstract

fetched live from OpenAlex

Cross-Border Governance has risen as an opportunity for rethinking integration and development at border regions. However, there is currently not consensus on what can be considered as “good” cross-border governance. From a systems approach, this paper proposes a theoretical framework by establishing a relationship among governance, integration and cooperation and propose criteria for evaluating governance models. The framework is used to analyze the Amazonian cross-border region among Peru, Brazil and Bolivia based on 44 surveys from key cross-border actors. Evidences showed that “environment-oriented” governance models in different scales (territorial, sectorial, etc.) have emerged from the mutual impact between cooperation initiatives and integration processes, where strengthening linkages among governance levels can generate better models.

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.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.066
GPT teacher head0.468
Teacher spread0.402 · 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