Quantifying subjective data using online Q-methodology software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Q-Sort methodology has been used to study participants’ subjective views on various topics ( Brown, 1996 ). The task has historically been completed by manually sorting cards into categories that force responses into a normal distribution ( Brown, 1996 ). Data collection using this method is time consuming and manual data entry is prone to human error. We describe here QMethod Software – a computerized web-based application that allows participants to sort and record their responses online. This online application eliminates the need for researchers to attend the study sessions and to manually enter data. QMethod Software described here is currently being used in both applied and cognitive psychology studies, including a clinical study that evaluates participants’ perception of behaviours seen as most characteristic or most uncharacteristic of psychological aggression or coercive control in situations of intimate partner violence. In a health psychology study, it is being used to examine people’s perceptions of food allergy, and in a psycholinguistics lab it was used to evaluate the affective valence, abstractness, and semantic richness ratings of words. We will show here that the data obtained from one of these psycholinguistic studies (abstractness/concreteness) correlates highly with existing measures ( Brysbaert, Warriner & Kuperman, 2014 ) thus demonstrating that the Q-sort methodology and this particular implementation, the QMethod Software app, reproduces more typical evaluations/assessments in the psycholinguistics literature.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it