Implementation of a machine learning algorithm for automated thematic annotations in avatar: A linear support vector classifier approach
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
Avatar Therapy (AT) is a modern therapeutic alternative for patients with schizophrenia suffering from persistent auditory verbal hallucinations. Its intrinsic therapeutical process is currently qualitatively analyzed via human coders that annotate session transcripts. This process is time and resource demanding. This creates a need to find potential algorithms that can operate on small datasets and perform such annotations. The first objective of this study is to conduct the automated text classification of interactions in AT and the second objective is to assess if this classification is comparable to the classification done by human coders. A Linear Support Vector Classifier was implemented to perform automated theme classifications on Avatar Therapy session transcripts with the use of a limited dataset with an accuracy of 66.02% and substantial classification agreement of 0.647. These results open the door to additional research such as predicting the outcome of a therapy.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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