In Search of Clarity: Self-Esteem and Domains of Confidence and Confusion
Bibliographic record
Abstract
To date, research suggests that self-concept clarity is a monolithic construct: Some people have clearly defined self-concepts in all domains, whereas others do not. The authors argued that self-concept clarity is instead multifaceted and varies across trait domains. The authors predicted that social commodities (SCs; e.g., looks, popularity, social skills) would show less self-concept clarity than would communal qualities (CQs; e.g., kindness, warmth, honesty), due to domain differences in observability, ambiguity, and controllability. Results replicated past findings that self-esteem predicts self-concept clarity but also demonstrated that participants' SC self-views were less clear than their CQ self-views. Moreover, people showed greater clarity about traits that were lower in observability and higher in ambiguity and controllability. These findings suggest that everyone, regardless of self-esteem, has self-concept domains of relative confidence and confusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".