Bibliographic record
Abstract
Computerized adaptive testing (CAT) is an efficient method to administer psychometric or educational tests and questionnaires. Unlike the standard fixed (“paper-and-pencil”) tests, items in a CAT are iteratively and optimally selected within a bank of available items, on the basis of previously administered items and the current ability estimate of the examinee. This general approach has several assets with respect to fixed tests: it reduces the risk of fraud, it allows for individualized questionnaires according to the examinee’s ability level, and fewer items must be administered to reach the same level of precision in the ability estimates. The purpose of this talk is threefold. First, a general overview of CAT is proposed and its main principles are quickly outlined. Second, a recently developed R package, called catR, is briefly presented and its functionalities are described. Finally, two applications are discussed. The first application is a live demonstration of catR, by using its by-default item bank about English aptitude assessment, and several CAT options. The second application focuses on the on-line testing platform Concerto, a web interface for the development and testing of CAT sessions that uses catR as underlying computational package. The R package catR was jointly developed with Gilles Raîche (Université du Québec à Montréal, Canada). The platform Concerto is under development by The Psychometrics Centre (Cambridge University, UK) under the supervision of Michal Kosinski and John Rust.
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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.082 | 0.090 |
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".