Extending information quality assessment methodology: A new veracity/deception dimension and its measures
Bibliographic record
Abstract
Abstract This paper extends information quality (IQ) assessment methodology by arguing that veracity/deception should be one of the components of intrinsic IQ dimensions. Since veracity/deception differs contextually from accuracy and other well‐studied components of intrinsic IQ, the inclusion of veracity/deception in the set of IQ dimensions has its own contribution to the assessment and improvement of IQ. Recently developed software to detect deception in textual information represents the ready‐to‐use IQ assessment (IQA) instruments. The focus of the paper is on the specific IQ problem related to deceptive messages and affected information activities as well as IQA instruments (or tools) of detecting deception to improve IQ. In particular, the methodology of automated deception detection in written communication provides the basis for measuring veracity/deception dimension and demonstrates no overlap with other intrinsic IQ dimensions. Considering several known deception types (such as falsification, concealment and equivocation), we emphasize that the IQA deception tools are primarily suitable for falsification. Certain types of deception strategies cannot be spotted automatically with the existing IQA instruments based on underlying linguistic differences between truth‐tellers and liars. We propose the potential avenues for the future development of the automated instruments to detect deception taking into account the theoretical, methodological and practical aspects and needs. Blending multidisciplinary research on Deception Detection with the one on IQ in Library and Information Science (LIS) and Management Information Systems (MIS), the paper contributes to IQA and its improvement by adding one more dimension, veracity/deception, to intrinsic IQ.
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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 | 0.041 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".