Creating Urban Social Capital: Some Evidence from Informal Traders in Nairobi
Bibliographic record
Abstract
The poverty and dramatic alteration in geographical composition of African cities have been associated with rapid urbanisation, the growth of the informal economy and migration. The latter has separated individuals from long-established social and kinship networks, and from familiar livelihood strategies. The sustainable livelihoods approach views social capital as one of the poor's most important assets in managing their lives. This paper asks four central questions. (1) Does the creation of new, urban forms of social capital, depend upon and deplete inherited forms? (2) Is social capital deliberately created or is it a by-product of sociability? (3) What are its functions in supporting the livelihoods of informal traders? (4) Is there a gender dimension to the strategies adopted? The paper draws on interviews with 124 traders in 2 Nairobi markets, and on key-informant interviews. Principal findings are that, whilst traders initially draw heavily on existing inherited social capital, they deliberately create and adapt their networks, opportunistically building relationships of trust in the marketplace which enable them to survive. The pace of change is different in different economic milieux. Women and men adopt different strategies to achieve similar ends. Conclusions are drawn for social capital theory and policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".