Examining the effects of gaming and guessing on script concordance test scores
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: In a script concordance test (SCT), examinees are asked to judge the effect of a new piece of clinical information on a proposed hypothesis. Answers are collected using a Likert-type scale (ranging from -2 to +2, with '0' indicating no effect), and compared with those of a reference panel of 'experts'. It has been argued, however, that SCT may be susceptible to the influences of gaming and guesswork. This study aims to address some of the mounting concern over the response process validity of SCT scores. METHOD: Using published datasets from three independent SCTs, we investigated examinee response patterns, and computed the score a hypothetical examinee would obtain on each of the tests if he 1) guessed random answers and 2) deliberately answered '0' on all test items. RESULTS: A simulated random guessing strategy led to scores 2 SDs below mean scores of actual respondents (Z-scores -3.6 to -2.1). A simulated 'all-0' strategy led to scores at least 1 SD above those obtained by random guessing (Z-scores -2.2 to -0.7). In one dataset, stepwise exclusion of items with modal panel response '0' to fewer than 10% of the total number of test items yielded hypothetical scores 2 SDs below mean scores of actual respondents. DISCUSSION: Random guessing was not an advantageous response strategy. An 'all-0' response strategy, however, demonstrated evidence of artificial score inflation. Our findings pose a significant threat to the SCT's validity argument. 'Testwiseness' is a potential hazard to all testing formats, and appropriate countermeasures must be established. We propose an approach that might be used to mitigate a potentially real and troubling phenomenon in script concordance testing. The impact of this approach on the content validity of SCTs merits further discussion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it