Characterization of Social Capital Using a Nested Latent Class Model: Case of Rural Areas in Central Malawi
Bibliographic record
Abstract
Social capital relates to capital created when a group of individuals or organizations develop the ability to work together for mutually productive gain. Gains in economic performance and innovative capacity depend on the institutional effectiveness of these relationships as measured by the available stock of social capital. Studies on social capital have however, been criticized for failing to account for the multi-dimensional and latent nature of the concept. Using household survey data from Malawi, this study uses latent class analytical methods to explore social capital and how it relates to welfare of people in rural communities in Malawi in Africa. It highlights the usefulness of latent class analytical methods for providing statistically valid information about the characteristics and determinants of social capital. Using the social capital dimensions of trust, participation and volunteering a four class LCA typology was constructed. Around 30% of the sample were classified as ‘trusty participants’, reporting active participation in the socio-economic activities of their communities and a high degree of community and institutional trust. Multinomial logistic regression revealed the covariates of the different typologies of social capital.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".