The Micro-Geography of Heterogenous Crime Experiences in the Township Informal Economy
Bibliographic record
Abstract
Crime is a major concern for entrepreneurs operating in the informal economy. Extant research has advanced our understanding of the socioeconomic and demographic factors that increase entrepreneurs’ susceptibility to crime. However, we still have limited knowledge of the role that physical space and place play in shaping how informal economy entrepreneurs experience crime. We use a unique 'small area census' dataset of entrepreneurs in a South African township to examine entrepreneurs’ experiences of crime, looking at several antecedents at the individual-, firm-, and contextual-level. Our findings suggest that there is significant heterogeneity among entrepreneurs in the informal economy in how they experience crime. Specifically, we identity spatially bound crime hotspots within the township and explore factors that lead entrepreneurs to report feeling relatively safe in areas that others consider to be high crime (safety mismatch) or entrepreneurs to report feeling unsafe in locations where others report crime not to be a significant issue (crime mismatch). We contribute to existing literature by advancing a micro-geographic perspective that shows how place and space intersects with individual and firm characteristics to explain how entrepreneurs experience the institutional pressure of crime differently.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".