“I'd like to be that attractive, but at least I'm smart”: How exposure to ideal advertising models motivates improved decision‐making
Bibliographic record
Abstract
Abstract The use of idealized advertising models has been heavily criticized in recent years. Existing research typically adopts a social comparison framework and shows that upward comparisons with models can lower self‐esteem and affect, as well as produce maladaptive behavior. However, the alternative possibility that consumers can cope with threatening advertising models by excelling in other behavioral domains has not been examined. The present research draws on fluid compensation theory (Tesser, 2000) and shows that idealized models motivate improved performance in consumer domains that fall outside that of the original comparison. These more positive coping effects operate through self‐discrepancies induced by idealized models, rather than self‐esteem or negative affect. Specifically, self‐discrepancies motivate consumers to improve decision‐making by: 1) making more optimal choices from well‐specified consideration sets, and 2) better self‐regulating indulgent choices. More broadly, the current research integrates and extends theories of fluid compensation and self‐discrepancy, as well as provides a more complete picture of the ways in which consumers cope with idealized advertising models.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".