The Tree That Hides the Forest? Testing the Construct Validity of ViCLAS through an Empirical Study of Missing Data
Bibliographic record
Abstract
This article tries to identify the reasons that explain the lack of data for some of the variables included in the police databases that are used by crime analysts to establish links between offences in order to detect serial offenders. It is based on an empirical analysis of the missing data in the cases introduced in the French Violent Crime Linkage Analysis System (ViCLAS) from 2006 to 2014. The findings show that the missing data are not randomly distributed, but vary according to the type of variable studied. The highest percentages of missing data are found in variables that refer to behavioural, physical, and distinctive characteristics; while the lowest percentages are found in variables that refer to sociodemographic, descriptive, and situational characteristics. The percentage of missing data increases in parallel with the level of complexity and subjectivity of the data requested, which raises questions about the construct validity of ViCLAS.
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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 | 0.097 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".