Measuring criminal attributions with a normative instructional set: Is there a difference?
Bibliographic record
Abstract
1Purpose . The purpose of this study was to evaluate the utility of the Criminal Attribution Inventory's (CRAI) normative instructional set (respondent's perception of the average type of crime) in assessing criminal attributions. Methods . To determine the role of the CRAI's instructional set, a content‐equivalent, personal instructional CRAI set was created. Correlations with an established criminal attribution scale (Blame Attribution Inventory [BAI]) and a measure of socially desirable responding (Paulhus deception scales [PDS]) were calculated. Partial correlations between the CRAI and BAI, controlling for the personal instructional CRAI, were calculated. Partial correlations were also calculated between the personal instructional CRAI and the BAI, controlling for socially desirable responding. Results . The normative instructional CRAI assessed similar domains as the personal instructional CRAI and made an additional contribution to criminal attributions. Socially desirable responding was minimally related to the normative instructional CRAI and could not account for the differences between the normative and personal instructional CRAI sets. Conclusions . The CRAI's normative instructional set assesses personal criminal attributions within the external blame domain assessing unique attributional variance. Such an instructional set has utility in assessing criminal attributions among those offenders who deny their offences.
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.
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 | 0.011 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".