The Role of Cultural Capital in Self-Reported Alexithymia and Empathy
Bibliographic record
Abstract
BACKGROUND: Sociocultural factors play an essential role in the way we process and express emotions. In this study, we asked whether Cultural Capital (CC)-the set of knowledge, cultural codes, and skills embodied by people-explains individual differences in two constructs measuring the capacity to understand our own emotions (alexithymia) or others' emotions (empathy). METHOD: A pre-registered survey was conducted with an Italian sample (N = 475). Alexithymia and empathy were assessed respectively via the Toronto Alexithymia Scale and the Interpersonal Reactivity Index. RESULTS: Regression analyses confirmed a significant, although limited, role of CC in predicting alexithymia and empathy. People with higher CC showed lower Externally Oriented Thinking, higher Perspective Taking, and higher Fantasy. Self-reported alexithymia and empathy were also impacted by scores on a social desirability scale. CONCLUSIONS: These results suggest that i) Cultural Capital influences the ability to analyse one's own feelings and understand others' perspectives, and ii) social desirability threatens the validity of self-report measures of emotional abilities. Overall, this research underlines the importance of studying affective processes by considering an individual's cultural context.
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.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".