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: Alexithymia is a subclinical experience in which individuals struggle to identify, distinguish, and describe their own emotions. It is most commonly measured with the self-reported Toronto Alexithymia Scale-20 (TAS-20). However, scholars hold different views on its structure, resulting in challenges in classifying individuals with alexithymia, which is detrimental to clinical diagnosis, counseling, and intervention. The present study aimed to investigate the types (or subgroups) of alexithymia within a sample of college students ( n = 707) from four Chinese universities. Two latent classes of three-factor two-class model solution were effectively identified by the Factor Mixture Model (FMM) approach: a “High-EOT alexithymia” class (18.2%) and a “Non-alexithymia” class (81.8%). The two subgroups exhibited similar performance in difficulty in identifying feelings (DIF) and difficulty in describing feelings (DDF), but they differed significantly in externally oriented thinking (EOT). This suggests that EOT might be a diagnostic criterion for alexithymia.
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 | 0.001 | 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) | 0.000 | 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