THE INFLUENCE OF MEDIA PORTRAYALS ON AGEISM: A CASE STUDY OF THE GOLDEN BACHELOR
Bibliographic record
Abstract
Abstract Background: Age-based discrimination or ageism is often propagated through media platforms (TV, news, social media). Focusing on the 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. Methods: Using social astronomy software we scraped Reddit’s English-language corpus for posts containing “golden bachelor” and extracted a sample of 1000 posts shared between August and September 2023. Qualitative content analysis of these posts revealed four forms of ageism (1) personal, (2) explicit, (3) implicit, and (4) benevolent. Results: Personal ageism emerged through comments devaluing older contestants’ appearance: “Hey at 75 you can go on The Golden Bachelor; wouldn’t that be fun. Wrinkles upon wrinkles…” Explicit ageism was evident in derogatory remarks targeting contestants’ age and limited physical capabilities: “Can the Golden Bachelor still get it up?”. Implicit ageism was discernible in subtle reinforcement of age-related stereotypes: “The guy who’s going to be the Golden Bachelor is 71 and he looks much younger than I remember 71-year-olds looking. He seems like a pretty normal guy.” and benevolent ageism manifested through the infantilization of older contestants: “cute old people love”. Discussion and Implications: 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 propagated 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".