Social Capital: Promise and Pitfalls of its Role in Development
Bibliographic record
Abstract
The purpose of this commentary is threefold. First, to review the origins and definitions of the concept of social capital as it has developed in the recent literature. Second, to examine the limitations of this concept when interpreted as a causal force able to transform communities and nations. Third, to present several relevant examples from the recent empirical literature on Latin American urbanisation and migration. These examples point to the significance of social networks and community monitoring in the viability of grass-roots economic initiatives and the simultaneous difficulty of institutionalising such forces. Current interest in the concept of social capital in the field of national development stems from the limitations of an exclusively economic approach toward the achievement of the basic developmental goals: sustained growth, equity, and democracy. The record of application of neoliberal adjustment policies in less developed nations is decidedly mixed, even when evaluated by strict economic criteria. Orthodox adjustment policies have led to low inflation and sustained growth in some countries, while in others they have failed spectacularly, leading to currency crises, devaluations, and political instability. The ‘one-size-fits- all’ package of economic policies foisted by the International Monetary Fund and the US Treasury on countries at very different levels of development have led to a series of contradictory outcomes that orthodox economic theory itself is incapable of explaining.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| 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".