Towards a Situated Spatial Epidemiology of Violence: A Placially-Informed Geospatial Analysis of Homicide in Alagoas, Brazil
Bibliographic record
Abstract
This paper presents an empirically grounded call for a more nuanced engagement and situatedness with placial characteristics within a spatial epidemiology frame. By using qualitative data collected through interviews and observation to parameterise standard and spatial regression models, and through a critical interpretation of their results, we present initial inroads for a situated spatial epidemiology and an analytical framework for health/medical geographers to iteratively engage with data, modelling, and the context of both the subject and process of analysis. In this study, we explore the socioeconomic factors that influence homicide rates in the Brazilian state of Alagoas from a critical public health perspective. Informed by field observation and interviews with 24 youths in low-income neighbourhoods and prisons in Alagoas, we derive and critically reflect on three regression models to predict municipal homicide rates from 2016-2020. The model results indicate significant effects for the male population, persons without elementary school completion, households with reported income, divorced persons, households without piped water, and persons working outside their home municipality. These results are situated in the broader socioeconomic context, trajectories, and cycles of inequality in the study area and underscore the need for integrative and contextually engaged mixed method study design in spatial epidemiology.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".