Fluctuations in self-esteem and paranoia in the context of daily life.
Bibliographic record
Abstract
Studies investigating the relationship between self-esteem and paranoia have specifically focused on self-esteem level, but have neglected the dynamic aspects of self-esteem. In the present article, the authors investigated the relationship between self-esteem and paranoia in two different ways. First, 154 individuals ranging across the continuum in level of paranoia were studied with the Experience Sampling Method (a structured self-assessment diary technique) to assess the association between trait paranoia and level and fluctuation of self-esteem in daily life. Results showed that trait paranoia was associated with both lower levels and higher instability of self-esteem. Second, the temporal relationship between momentary (state) paranoia and self-esteem was investigated in the daily life of these individuals. Results showed that a decrease in self-esteem was associated with an immediate increase in paranoia. The findings indicate that paranoid individuals are not only characterized by a lower level of self-esteem but also by more fluctuations in their self-esteem and that fluctuations in self-esteem predict the degree of subsequent paranoia. These results are consistent with the hypothesis that paranoia is associated with dysfunctional strategies of self-esteem regulation.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".