From Zoomers to Geezerade: Representations of the Aging Body in Ageist and Consumerist Society
Bibliographic record
Abstract
This paper is based on an analysis of representations of seniors in the media. In particular, we examine images of the bodies of seniors in the advertising campaigns promoting a product called Geezerade sold in Circle K convenience stores in the Atlantic provinces of Canada in the summer of 2011. We contrast these with images of seniors in the Canadian magazine Zoomer, formally CARP magazine, a magazine published by the Canadian Association of Retired People, a seniors advocacy organization. Following Goffman’s arguments in his seminal presidential address to the American Sociological Association, “the Interaction Order”, we take the position in this analysis that the body does not determine social practices but none-the-less the body is the sign vesicle that enables interaction. Concomitant however, while the images of bodies we see in the media do not determine the signs given and given off via bodily presentation, they none-the-less provide us with the categories by which we interpret those signs. We conclude that the images in the Geezerade campaign and Zoomer magazine represent a binary model of images of seniors that reflects ageist and classist assumptions about the bodies of seniors. Such a model limits the categories through which we understand the aging body and fails to account for the diversity of seniors’ bodies in society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".