Bibliographic record
Abstract
BACKGROUND: We explored the relationship between alexithymia and interpersonal behavior, particularly the expression of anger. METHODS: Ninety-eight college students completed the Toronto Alexithymia Scale. A median split was used to divide participants into a low-alexithymia and a high-alexithymia group. The experimenter intentionally engaged in a series of anger-provoking behaviors. RESULTS: Compared to individuals in the low-alexithymia group, individuals in the high-alexithymia group were more interpersonally avoidant and exhibited more nonverbal anger, yet there was a trend for them to describe their lab experience as more pleasant. Among individuals in the high-alexithymia group, the different measures of anger and interpersonal behavior were less strongly associated than they were among individuals in the low-alexithymia group. CONCLUSIONS: The results provide evidence of a complex association between alexithymia and anger, and of the lack of coherence in the communication of individuals with high levels of alexithymia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".