Examining the Connection Between Missing Persons and Victimization: An Application of Lifestyle Exposure Theory
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
Victimization has been anecdotally connected to missing persons within several reviews, inquiries, and media stories, particularly in consequence of missing-turned-serial murder cases. However, this has been paid little attention within the scholarship. To remedy this gap, this study empirically explores the link between missing persons and victimization through the perspective of lifestyle exposure theory. A qualitative thematic analysis of 1,920 missing person files uncovers several demographic and lifestyle factors implicating victimization risk, as well as their ranked aggregated and disaggregated saliency. Examples include criminality, victimizing events, sex work, and gender identity. Also discovered is that the context and nature of victimization risk differ for specific people and groups. The implications of these findings and future research areas are herein discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| 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