Geographic profiling: the fast, frugal, and accurate way
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
Abstract The current article addresses the ongoing debate about whether individuals can perform as well as actuarial techniques when confronted with real world, consequential decisions. A single experiment tested the ability of participants ( N = 215) and an actuarial technique to accurately predict the residential locations of serial offenders based on information about where their crimes were committed. Results indicated that participants introduced to a ‘Circle’ or ‘Decay’ heuristic showed a significant improvement in the accuracy of predictions, and that their post‐training performance did not differ significantly from the predictions of one leading actuarial technique. Further analysis of individual performances indicated that approximately 50% of participants used appropriate heuristics that typically led to accurate predictions even before they received training, while nearly 75% improved their predictive accuracy once introduced to either of the two heuristics. Several possible explanations for participants' accurate performances are discussed and the practical implications for police investigations are highlighted. Copyright © 2004 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it