The bounds of cognitive heuristic performance on the geographic profiling task
Bibliographic record
Abstract
Abstract Human performance on the geographic profiling task—where the goal is to predict an offender's home location from their crime locations—has been shown to equal that of complex actuarial methods when it is based on appropriate heuristics. However, this evidence is derived from comparisons of ‘X‐marks‐the‐spot’ predictions, which ignore the fact that some algorithms provide a prioritization of the offender's area of spatial activity. Using search area as a measure of performance, we examine the predictions of students ( N = 200) and an actuarial method under three levels of information load and two levels of heuristic‐environment fit. Results show that the actuarial method produces a smaller search area than a concentric search outward from students' ‘X‐marks‐the‐spot’ predictions, but that students are able to produce search areas that are smaller than those provided by the actuarial method. Students' performance did not decrease under greater information load and was not improved by adding a descriptive qualifier to the taught heuristic. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".