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Record W4327896567 · doi:10.1111/jopy.12832

Self‐concealment, secrecy, and guilt

2023· article· en· W4327896567 on OpenAlexaff
Christopher G. Davis

Bibliographic record

VenueJournal of Personality · 2023
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologySecrecyFeelingMediationAffect (linguistics)Social psychologyTraitSelf-disclosureSet (abstract data type)RomanceDevelopmental psychologyPsychoanalysisComputer security

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.063
GPT teacher head0.383
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2023
Admission routes1
Has abstractyes

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