Bibliographic record
Abstract
Much psychological research makes use of explicit measures (e.g., questionnaires), where the participants are asked to report about themselves. Such measures at best index various aspects of the person’s conscious self-concept. However, there are also implicit measures that are designed to tap similar constructs in a more indirect way (e.g., the Implicit Association Test and the Thematic Apperception Test). In a number of different areas (e.g. memory, motivation, attitudes), explicit and implicit measures of psychological constructs have been found (a) to show at best very weak intercorrelations, and (b) to correlate with different variables, indicating that they measure different things. Although there is less research on explicit vs. implicit measures of alexithymia, the same kind of dissociation can be seen also in this area. It is argued that self-assessment measures like the Toronto Alexithymia Scale (especially Factors 1 and 2 of the TAS-20) are at best valid measures of people’s meta-emotional self-efficacy, that is, an aspect of their conscious self-concept. Relying on the TAS-20 as the measure of alexithymia is therefore likely to be grossly misleading. It is suggested that more effort should be devoted to the development and testing of various kinds of implicit measures 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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".