Functionality of the Crosswise Model for Assessing Sensitive or Transgressive Behavior: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Tools for reliable assessment of socially sensitive or transgressive behavior warrant constant development. Among them, the Crosswise Model (CM) has gained considerable attention. We systematically reviewed and meta-analyzed empirical applications of CM and addressed a gap for quality assessment of indirect estimation models. Guided by the PRISMA protocol, we identified 45 empirical studies from electronic database and reference searches. Thirty of these were comparative validation studies (CVS) comparing CM and direct question (DQ) estimates. Six prevalence studies exclusively used CM. One was a qualitative study. Behavior investigated were substance use and misuse (k= 13), academic misconduct (k= 8), and corruption, tax evasion, and theft (k= 7) among others. Majority of studies (k= 39) applied the “more is better” hypothesis. Thirty-five studies relied on birthday distribution and 22 of these usedP= 0.25 for the non-sensitive item. Overall, 11 studies were assessed as high-, 31 as moderate-, and two as low quality (excluding the qualitative study). The effect of non-compliance was assessed in eight studies. From mixed CVS results, the meta-analysis indicates that CM outperforms DQ on the “more is better” validation criterion, and increasingly so with higher behavior sensitivity. However, little difference was observed between DQ and CM estimates for items with DQ prevalence estimate around 50%. Based on empirical evidence available to date, our study provides support for the superiority of CM to DQ in assessing sensitive/transgressive behavior. Despite some limitations, CM is a valuable and promising tool for population level investigation.
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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 | 0.249 | 0.387 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.024 | 0.069 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".