Bibliographic record
Abstract
OBJECTIVE: Individuals with a tendency to conceal unflattering information about themselves are more likely to be preoccupied by their secrets and tend to report more negative affect. According to theory, this negative affect is due to self-concealers' conflicting motivation to be authentic in their relationship but fear the negative consequences should they reveal their secrets, which promotes ill-fated attempts to suppress. The purpose of the current study was to test a central component of this model. METHODS: = 39.6, SD = 11.9) were surveyed on four biweekly occasions. Multilevel mediation analyses were conducted to test whether preoccupation and suppression mediated the link between self-concealing and negative affect and guilt. RESULTS: The data support the hypotheses. Self-concealers were more preoccupied with and prone to suppress their secret than those low on the trait, which, in turn, predicted greater negative affect and guilt. CONCLUSION: The findings suggest that self-concealers' insecurities and fear of the relational consequences of disclosure set the stage for the debilitating cycle of suppression and preoccupation that leaves them feeling anxious and guilty.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".