Positive and Negative Self-Worth Beliefs and Evaluative Standards
Bibliographic record
Abstract
This study examined several different types of self-worth beliefs and evaluations as predictors of depression and self-esteem. Based upon a self-schema model of emotion, some of these beliefs and evaluations had a traditional negative focus (e.g., “I am failing at work”), whereas others focused specifically on positive aspects of self-worth (e.g., “I am able to give, as well as receive, in relationships”). Findings indicated that positive self-worth evaluations were one of the main predictors of greater self-esteem and less depression, thus indicating a need for further exploration of the role of positive evaluative components of the self-schema on psychological well-being. Our findings also revealed that self-worth beliefs and evaluative standards pertaining to independence and a sense of mastery over one’s environment were generally better predictors of well-being than those pertaining to relationships with others. This pattern was particularly evident for self-esteem, and supports the distinction made in the self-schema model between self-worth based upon individualism versus relatedness themes. Finally, we compared the relative predictive utility of the self-schema model with a self-worth contingency model advanced by Crocker. Here, the beliefs and evaluations specified in the self-schema model were significant predictors of well-being, above and beyond the specific content domains specified in the self-worth contingency model.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".