Social Capital in Firm-Stakeholder Networks: A Corporate Role in Community Development
Bibliographic record
Abstract
Corporations can contribute to sustainable development goals like poverty reduction by bringing linking social capital into community and stakeholder networks. Often their well-intentioned efforts produce disappointing results because they encounter a variety of pitfalls like unorganised communities, self-serving elites, violent opposition, and conflicting stakeholder demands. This article applies the social network analysis concepts of social capital, bridging, bonding, and core-periphery structure to firm-stakeholder networks. The result is a three-dimensional classification scheme showing 12 patterns of social capital. It is proposed that each of the 12 is associated with a different pattern of outcomes for the stakeholders and the company, exemplified by the aforementioned pitfalls. Measures of the stakeholder network's current pattern of social capital can be compared with the 12 classification patterns to find the closest match. It is proposed that a match predicts pitfalls and therefore can guide movement towards the patterns that most facilitate sustainable development.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".