Networks of Cross-Border Non-State Actors: The Role of Social Capital in Regional Integration
Bibliographic record
Abstract
This paper examines the contribution of networks of cross-border grassroots non-State actors to regional integration. It uses three assumptions to determine whether sub-regional schemes augment regional integration: (a) networks of grassroots non-State actors connect communities that share common backgrounds, histories and cultures; (b) interactions in the networks generate a trust that stabilizes them and contributes to network efficiency; and (c) where these networks straddle State boundaries, they integrate the economies that host the communities of actors in the networks and thus enhance integration. The paper achieves its objective by illustrating these assumptions in the context of sub-regional integration in Southeast Asia and Southern Africa. A thorough review of the literature on regional and sub-regional integration, borderland studies, etc. is conducted along with the use of social capital and historical, socioeconomic and political accounts to illustrate the role of informal networks in integration. Because networks, norms and trust dominate conceptual discussion of social capital (Schuller, T., S. Baron, and J. Field. 2000. Social capital: A review and critique. In Social capital: Critical perspectives, eds. S. Baron, J. Field, and T. Schuller, 1–38. Oxford: Oxford University Press.), the paper conceptualizes the terms in the context of social capital. Participant observations, face-to-face interviews and focus group discussions conducted during extensive fieldwork between September 2013 and November 2014 at selected border posts, in the major border towns of the adjacent provinces of the ZMM-GT, in markets and villages in the contiguous border areas of the growth triangle also provide the primary data employed in the analysis. Sub-regional initiatives contribute to development, as does macro-regionalism. Unlike Southeast Asians, people in southern Africa are primarily driven by the need for survival and operate less on ethnic lines. However, a clear demonstration of social capital and cohesion is evident here. Leaders in Africa should encourage cross-border ethnic and kinship ties rather than abuse ethnicity for political gain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".