Bibliographic record
Abstract
Abstract. While shame is sometimes discussed as a key element at the core of personality pathologies, its relationship with pathological personality traits is still understudied. Previous research suggested that shame is a common subjective experience in patients with borderline and narcissistic personality traits. However, little is known about how borderline and narcissistic traits are associated with specific areas of shame in community samples. The present study aims to investigate these associations, using a dual strategy, that is, both at “variable-level” using correlational analyses and at “person-level” using a cluster-analytic strategy with borderline and narcissistic (grandiose and vulnerable) personality traits as clustering variables. A total of 254 French-Canadian adult participants were recruited to complete an online battery of self-report questionnaires. Correlational analyses revealed that borderline-related traits and narcissistic vulnerability showed some significant and meaningful differences pertaining to Behavioral shame while sharing a similar pattern of associations with Characterological and Bodily shame. Alternatively, shame does not appear to be a strong correlate of narcissistic grandiosity, although some significant – and somewhat unexpected – positive associations between the two were found. Cluster analysis yielded four groups based on their levels of pathological traits; the groups showed indiscriminate associations with different shame areas, suggesting that the association between shame and pathological traits is more global and less area specific.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".