Paradiplomacy, Its Actors and Trends: State of the Art and Additional Contributions
Bibliographic record
Abstract
Abstract The current pandemic scenario has brought relevance to the paradiplomatic activities carried out by subnational entities, insofar as it has challenged the traditional models of International Relations, pushing decentralized actors to seek solutions to their problems through external agreements. Despite its importance, scientific production on the subject of paradiplomacy is scattered. In this sense, we sought to analyse the state of the art of current scientific production on paradiplomacy, as well as whether this production is characterized by a stop-and-go trend, as seen in the empirical logic of the international activities of sub-national entities. To this end, bibliometric analysis and a systematic literature review were carried out based on 75 scientific papers available on the Web of Science (WOS), Scopus and the Scientific Electronic Library (Scielo). The main results, therefore, pointed to a stop-and-go trend in scientific production on paradiplomacy, which in turn was driven by the situation imposed by the COVID-19 pandemic (2021), and the rise of studies on paradiplomatic activities carried out by Chinese (2021 and 2020), Canadian (2019) and, above all, Latin American (2018) subnational entities. Finally, as a contribution to the literature on the subject, the use of a little-used methodology to examine the role of subnational entities in International Relations is verified, which therefore provides an alternative way to interpret the results and present new insights on the subject.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.034 | 0.065 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".