Age and Antiaging Technique Influence Reactions to Age Concealment
Bibliographic record
Abstract
OBJECTIVES: Despite the rapid expansion of the antiaging cosmetic industry in recent years, little is known about the current social judgment consequences of concealing one's age. In two studies, we examined perceivers' evaluations and mental representations of individuals who engage in age concealment. METHODS: In Study 1, we assessed young and older adults' reactions toward a middle-aged or older adult target who engaged in mild or major forms of age concealment. In Study 2, we examined the social consequences of age concealment in greater detail by including younger middle-aged targets and expanding the range of concealment procedures used. RESULTS: Targets received less favorable evaluations (a) to the extent that they engaged in invasive procedures, (b) when they were viewed by young adults rather than by older adults, and to some degree, (c) if they were middle-aged adults rather than older adults. Participants held different expectations concerning aging and age concealment depending on the age of the target and the antiaging technique used. DISCUSSION: These findings suggest that reactions to age concealment vary according to the concealment technique used, the age of the perceiver, and to some extent, the age of the target.
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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 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".