Survivalist Organizing in Urban Poverty Contexts
Bibliographic record
Abstract
Institutional scholarship on organizing in poverty contexts has focused on the constraining nature of extant institutions and the need for external actors to make transformative change interventions to alleviate poverty. Comparatively little attention has been paid to the potentially enabling nature of extant institutions in poverty contexts. We argue that more empirical work is needed to deepen our understanding of self-organizing processes that actors embedded in such contexts generate in their own efforts to survive. Drawing on the social worlds approach to institutional analysis, we shed light on how actors self-organize to produce enduring organizational arrangements to safeguard themselves against adverse poverty outcomes. Employing data from fieldwork and interviews collected in the urban neighborhood of Dagoretti Corner in Nairobi, Kenya, we examine the colocation of 105 largely identical auto repair businesses in close spatial proximity. We find that actors leverage an indigenous institution—the societal ethos of Harambee—to enable a process we identify as “survivalist organizing.” Based on our research, we argue that survivalist organizing incorporates four interlocking survival mechanisms: cultivating interbusiness solidarity, maintaining precarious interbusiness relationships, redistributing resources to prevent business deaths, and generating collective philanthropy to avoid personal destitution. We develop a new research agenda on the institutional study of self-organizing in poverty contexts focused on strengthening rather than supplanting urbanized indigenous institutions that catalyze collective self-organizing. Funding: This work was supported by the China National Science Foundation [Grants 72091310 and 72091315].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| 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".