The “power few” of missing persons’ cases
Bibliographic record
Abstract
Purpose The purpose of this paper is to test the “power few” concept in relation to missing persons and the locations from which they are reported missing. Design/methodology/approach Data on missing persons’ cases ( n = 26,835) were extracted from the record management system of a municipal Canadian police service and used to create data sets of all of the reports associated with select repeat missing adults ( n = 1943) and repeat missing youth ( n = 6,576). From these sources, the five locations from which repeat missing adults and youth were most commonly reported missing were identified (“power few” locations). The overall frequency of reports generated by these locations was then assessed by examining all reports of both missing and repeat missing cases, and demographic and incident factors were also examined. Findings This study uncovers ten addresses (five for adults; five for youths) in the City from which this data was derived that account for 45 percent of all adults and 52 percent of all youth missing person reports. Even more striking, the study data suggest that targeting these top five locations for adults and youths could reduce the volume of repeat missing cases by 71 percent for adults and 68.6 percent for youths. In relation to the demographic characteristics of the study’s sample of adults and youths who repeatedly go missing, the authors find that female youth are two-thirds more likely to go missing than male youth. Additionally, the authors find that Aboriginal adults and youths are disproportionately represented among the repeat missing. Concerning the incident factors related to going missing repeatedly, the authors find that the repeat rate for going missing is 63.2 percent and that both adults and youths go missing 3–10 times on average. Practical implications The study results suggest that, just as crime concentrates in particular spaces among specific offenders, repeat missing cases also concentrate in particular spaces and among particular people. In thinking about repeat missing persons, the present research offers support for viewing these concerns as a behavior setting issue – that is, as a combination of demographic factors of individuals, as well as factors associated with particular types of places. Targeting “power few” locations for prevention efforts, as well as those most at risk within these spaces, may yield positive results. Originality/value Very little research has been conducted on missing persons and, more specifically, on how to more effectively target police initiatives to reduce case volumes. Further, this is the first paper to successfully apply the concept of the “power few” to missing persons’ cases.
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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 | 0.000 | 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 | 0.000 |
| 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 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".