Analyzing how a Social Base Impacts Economic Development and Competitiveness Strategies in a Cross-border Context: the Case of Region Laredo
Bibliographic record
Abstract
It has been said that, “borders are the scars of history” (Schuman n.d. French Statesman, Founder of European Union), and while that may be true, borders might also be considered as living labs in which social interactions and the ability to coexist ultimately shape economic, social, and political prosperity. The socially-driven concepts of Social Capital, and more recently Social Innovation, are the basis of extensive research across a broad scope of academic arenas. From clusters (Wolfe 2002. Knowledge, Learning and Social Capital in Ontario’s ICT Clusters. Paper Presented at the Annual Meeting of the Canadian Political Science Association, Toronto, May (http://www.utoronto.ca/progris/pdffiles/Ontario%27s%20ICT%20Clusters.pdf)) to health care (Global Health Innovation Guidebook), Social Capital and Social Innovation are increasingly considered as tools central to the creation of improved living environments and strong communities. The objective of this paper is to explore the impact that Social Capital and Social Innovation (a Social Base) have on economic development and competitiveness strategies in a cross-border context. To this end and through the application of our analytical framework, we set out to test how these social dynamics and links impact economic development and competitiveness strategies, specifically within Region Laredo.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".