A Generalization of the «Lady-Tasting-Tea» Procedure to Link Qualitative and Quantitative Approaches in Psychiatric Research
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
In Fisher’s “The Design of Experiments”, a trial was designed to test a lady’s claim to be able to discriminate whether the milk or the tea was added first to a cup. In this trial, eight cups are poured, four with milk first and four with tea first. They are then presented in random order to a subject who has to divide them into two sets of 4, according his/her belief about the "treatment" received. The present paper generalizes this design so that a hypothesis concerning the existence of two sub groups in a set of psychiatric patient records (whether written, audiotaped or videotaped) can be tested rigorously from a statistical point of view. Tables are proposed to enable power and sample size calculations. A real example is presented; it shows that psycho-dynamically oriented professionals are able to discriminate seven healthy adults who have experienced a sibling’s cancer during childhood or adolescence from seven matched controls. This method is particularly suited to small sample studies that explore elusive clinical hypotheses traditionally tackled with qualitative methodologies.
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.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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