The State at the Margins: The Politics of State Social Assistance and Citizenship in Rural Kenya and Tanzania
Bibliographic record
Abstract
This dissertation leverages both inter-state and intra-state comparisons to interrogate why cash transfer programs in Kenya and Tanzania have had profoundly different impacts on recipients’ perceptions and practices of citizenship. First, it leverages intra-state comparisons to compare Luo communities receiving government cash transfers with those not receiving cash transfers to examine the impacts of cash transfers of citizens’ perceptions of and access to the structures of the state. It finds that cash transfers have had transformative impacts for citizens’ perceptions and practices of citizenship in Kenya but not in Tanzania. Second, it leverages inter-state comparisons amongst these Luo communities on either side of the Tanzania-Kenya border, who have a shared history language and culture, to elucidate why similar cash transfer programs have had profoundly different impacts on citizenship. This dissertation argues that the impacts of cash transfer programs are mediated by post-colonial histories of national building which structure the formal institutions through which cash transfers are implemented and the informal institutions that shape commonly held understandings of the state’s role vis-à-vis the citizen. Where citizens have been historically excluded from the central resources of the state, the state provision of cash transfers can provide citizens new avenues through which they can interact with and make demands on the state. In Tanzania, the post-colonial nation-building project created an inclusive sense of citizenship that is deeply rooted in a cohesive national identity, perceptions of community and norms of reciprocity. The introduction of means-tested cash transfer programs in Tanzania, then, did not challenge commonly held understandings of citizenship and of the state’s role vis-à-vis the citizen. In contrast, in Kenya, a post-colonial era marked by the distribution of state resources through patronage networks, exclusionary economic and political policies that discriminated based on ethnicity created an exclusive, sense of citizenship, which is directly tied to the individual and their relationship to various patrons. However, the introduction of cash transfer programs, distributed based on need rather than patronage, has led to a gradual reconceptualization of citizenship towards one rooted in reciprocal rights and duties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".