Tough Ties and Rough Networks: Inequality and Exploitation in African Slums
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reciprocity and negativity in social relationships are fundamental topics of social research rarely examined in sub‐Saharan Africa. Since the number and quality of relationships is associated with individual outcomes, these ties are particularly important in impoverished areas. We conducted a multi‐method study of the conditions associated with problematic networks and difficult people, including face‐to‐face surveys in Agbogbloshie (Accra, Ghana) and Kangemi (Nairobi, Kenya). While one quarter of all relationships were perceived to be difficult, results reveal significant differences between the two communities in terms of the composition of personal networks and the factors associated with difficulty at both relational and network levels of analysis. Kenyan networks are more difficult when there is an imbalance of assistance provided by the respondent (exploitation), while any imbalance (inequality) is problematic in Ghanaian networks. These findings underscore the importance of independent analyses of negative ties and difficult networks, contributing to a community‐based understanding of poverty in urban slums.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it