When the Measure Affects the Outcome more than the Intervention: The Unexpected Effect of Object Differences on Alternate Uses Test (AUT) Scores
Bibliographic record
Abstract
The alternate uses test (AUT) is a divergent thinking measure wherein participants are asked to produce creative uses for common objects. A study done during my undergraduate degree revealed a positive effect of conflict-processing on AUT scores. During my master’s degree, studies were conducted to expand on and replicate this effect, but were unsuccessful. Unexpectedly, there was an effect of AUT object on AUT scores, suggesting that a characteristic of the measure (i.e., the object chosen to produce uses for in the AUT) affected the outcome non-uniformly. This object effect is concerning for studies that rely on the AUT, as AUT objects are not chosen consistently across studies. Furthermore, many studies do not analyze object differences, which can be problematic if preventative measures are not taken. A study is proposed to address this concern by standardizing AUT object usage with objects that have non-significant differences in AUT scores.
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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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".