Comparison of Methods for Collecting and Modeling Dissimilarity Data: Applications to Complex Sound Stimuli
Why this work is in the frame
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Bibliographic record
Abstract
Sorting procedures are frequently adopted as an alternative to dissimilarity ratings to measure the dissimilarity of large sets of stimuli in a comparatively short time. However, systematic empirical research on the consequences of this experiment-design choice is lacking. We carried out a behavioral experiment to assess the extent to which sorting procedures compare to dissimilarity ratings in terms of efficiency, reliability, and accuracy, and the extent to which data from different data-collection methods are redundant and are better fit by different distance models. Participants estimated the dissimilarity of either semantically charged environmental sounds or semantically neutral synthetic sounds. We considered free and hierarchical sorting and derived indications concerning the properties of constrained and truncated hierarchical sorting methods from hierarchical sorting data. Results show that the higher efficiency of sorting methods comes at a considerable cost in terms of data reliability and accuracy. This loss appears to be minimized with truncated hierarchical sorting methods that start from a relatively low number of groups of stimuli. Finally, variations in data-collection method differentially affect the fit of various distance models at the group-average and individual levels. On the basis of these results, we suggest adopting sorting as an alternative to dissimilarity-rating methods only when strictly necessary. We also suggest analyzing the raw behavioral dissimilarities, and avoiding modeling them with one single distance model.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| grok | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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