Cross-Domain Effects of Guilt on Desire for Self-Improvement Products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research examines the notion that guilt, the negative emotion stemming from a failure to meet a self-held standard of behavior, leads to preferences for products enabling self-improvement, even in domains unrelated to the original source of the guilt. Examining consumer responses to real products, this research shows that such effects arise because guilt—by its focus on previous wrongdoings—activates a general desire to improve the self. This increase in desire for self-improvement products is only observed for choices involving the self (not others), is not observed in response to other negative emotions (e.g., shame, embarrassment, sadness, or envy), and is mitigated when people hold the belief that the self is nonmalleable. Building on past work that focuses on how guilt often leads to the motivation to alleviate feelings of guilt either directly or indirectly, the current research demonstrates an additional, novel downstream consequence of guilt, showing that only guilt has the unique motivational consequence of activating a general desire to improve the self, which subsequently spills into other domains and spurs self-improving product choices. These findings are discussed in light of their implications for research on the distinct motivational consequences of specific emotions and on consumer well-being.
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.
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 | 0.004 | 0.001 |
| 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 | 0.000 |
| 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 it