Urban trust in Kenya and Tanzania: Cooperation in the provision of public goods
Bibliographic record
Abstract
Literature connecting ethnic diversity with public goods provision has found public goods to be poorly and unevenly supplied in ethnically heterogeneous communities. Scrutinising this hypothesis, the study contrasts an ethnically homogenous community in Kenya with an ethnically heterogeneous one in Tanzania, documenting levels of trust and cooperation in public goods provision. Interviews and focus groups with market-sellers of Mwanza (Tanzania) and Kisumu (Kenya) reveal how the two professionally similar populations differ starkly in the way they participate in public goods, and in an opposite direction to that which would be predicted by the current literature on ethnicity. On the topic of the organisation of security and cleaning within markets in Mwanza, ethnically heterogeneous market-sellers' sense of solidarity facilitates a greater degree of seller-on-seller trust. In Kisumu, in contrast, with participants reflective of the dominant Luo ethnicity, the lack of state provision of public services has seen a feeble and individualistic response. The findings demonstrate how ethnic distribution matters less for public goods provision than commitments amongst citizens themselves and between citizens and local authorities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".