New love, old stereotypes: ageism in social media discourses on the golden bachelor
Bibliographic record
Abstract
Age-based discrimination or ageism is often propagated through media platforms. Focusing on the 2023 reality television show “The Golden Bachelor”, which features older adult contestants seeking romance, this study explored ageism spread via social media posts related to the program. Using social astronomy software and qualitative content analysis of user posts, four forms of ageism (1) personal, (2) explicit, (3) implicit, and (4) benevolent, were explored in a sample of 4000 posts shared between November 2023 and June 2024 in Reddit’s English-language corpus. The analysis of Reddit posts related to “The Golden Bachelor” revealed four key themes: (1) Dimensions of Ageism—discussions highlighted overt and subtle forms of ageism, including personal, explicit, and benevolent biases; (2) Aesthetic and Gendered Expectations in Aging—Reddit users critiqued societal pressures for older women to maintain youthful appearances; (3) Sexuality and Romance in Older Adults—while some users expressed skepticism about older adults’ romantic pursuits, others celebrated the cast members’ desires for love and intimacy; (4) The Convergence of Ageism and Sexism—comments reflected a gendered lens, where older women faced harsher scrutiny compared to their male counterparts. While “The Golden Bachelor” may help some understand the capabilities of older adults and counter the misperception of them as asexual, existing ageist stereotypes emerge and are shared on social media platforms. These findings underscore the pervasive nature of ageism in social media and highlight the importance of addressing age-related biases in media representation.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".