Visual representation of age groups as a function of ageism levels
Bibliographic record
Abstract
Prejudice against the elderly is a growing concern and has shown to report many negative social and individual consequences (European social survey, 2012). Last VSS (Dion-Marcoux et al., 2016), we presented a study showing that ageism modulates the mental representation of a prototypical young and old face: individuals with higher prejudice represented a young face as being older and an old face as being younger than individuals with less prejudice. The present study verified if this finding is subtended by ageism modifying the boundaries used to categorize a person as young or old, or by ageism modifying the representation of facial aging throughout life. Thirty young adults took part in three tasks: An Implicit Association Test, an age categorization task, and a Reverse Correlation task. In the Reverse Correlation task, participants had to decide which of three faces embedded in white noise was most prototypical of the appearance of a 20, 40, 60 or 80 years-old face (block design). The mental representations of the ten participants with the highest vs. lowest ageism were averaged, and presented to 30 individuals who estimated their age. Results show a significant interaction between ageism and face group on the perceived age [F(3, 87)=17.17, p< 0.05]. Although participants with higher prejudice had a significantly older perception of the age 40 [t(58)=3.077, p=0.0032], the pattern reversed for 80 years-old faces [t(58)=-2.317, p=0.024], which they represented as younger. The boundary used in the age categorization task did not differ as a function of ageism [t(18)=0.18, ns]. These results suggest that highly prejudiced individuals represent different groups (40, 60 and 80 years-old) of other-age faces as being less dissociable from one another than lower prejudice individuals. Meeting abstract presented at VSS 2017
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".