Applying the Daubert Factors to IOP-29-Based Testimony
Bibliographic record
Abstract
Abstract This article examines the Inventory of Problems – 29 (IOP-29) in terms of the Daubert standards. The three majority opinions (i.e., Daubert v. Merrell Dow Pharmaceuticals, General Electric Co. v Joiner, and Kumho Tire Co. v. Carmichael) that constitute the so-called Daubert trilogy represented a sea change in judicial awareness of the relevance of empirical methodology in providing probative expert testimony in court. Specifically, they brought forth a set of factors that may be considered during trial to assess the admissibility of proffered expert testimony. The first section of the present article briefly describes the current state of negative impression management assessment in psycho-legal context, with particular reference to the development of the procedures and expectations for expert testimony. Next, Daubert factors are defined and applied, one by one, to IOP-29-based testimony. This led to the conclusion that the IOP-29 has been thoroughly and empirically tested in different contexts and countries, and across different psychopathological conditions (1st Daubert standard); it has been peer-reviewed and publications on it have showed continued growth in recent years (2nd Daubert standard); based on the many empirical studies, its error rate is potentially knowable (3rd Daubert standard); this information, along with standards for controlling its operation, are available in the IOP-29 Professional Manual (4th Daubert standard); there is growing evidence of the general acceptance that the IOP-29 has received within the scientific community currently and over the years (5th Daubert standard). Finally, recommendations are proposed on the use of the test within the forensic field.
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 | 0.046 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".