Self-discrepancies and negative affect: A primer on when to look for specificity, and how to find it
Bibliographic record
Abstract
There is substantial evidence that discrepancies within the self-system produce emotional distress. However, whether specific types of discrepancy are related to different types of negative affect remains contentious. At the heart of self-discrepancy theory (SDT: Higgins, 1987, 1989) is the assumption that different types of discrepancies are related to distinctive emotional states, with discrepancies between the actual and ideal selves being uniquely related to dejection-related emotion and discrepancies between the actual and ought selves being uniquely related to agitation-related emotion. Research examining this proposition has demonstrated that the magnitudes of these discrepancies are substantially correlated. As a result, some researchers have questioned whether they are functionally independent (e.g., Tangney, Niedenthal, Covert, & Barlow, 1998). In addition, other researchers have failed to support the hypothesized unique relationships (e.g., Ozgul, Heubeck, Ward, & Wilkinson, 2003). Together these two types of research finding have been interpreted as presenting a challenge to SDT. It is our contention that this interpretation is inaccurate. In this paper, we review the assumptions made when testing for these distinct relationships. Specifically, we examine the necessary conditions under which the functional independence of discrepancies is apparent, and the statistical methods appropriate to test these relationships. We also comment on the measurement of self-discrepancies, and fundamental problems in the interpretation of null findings. We conclude that studies using appropriate methodological and statistical procedures have produced ample evidence that discriminant relationships exist, and we encourage researchers to further investigate the conditions under which these relationships are most apparent.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".