A food blog created by and for elders: a political gesture informed by the normative injunctions to eat and age well
Bibliographic record
Abstract
This article analyzes a food blog developed by and for elders. The organization under study developed this project to integrate itself in a mediatized food culture. Rather than to perpetuate the individualising processes that come with the pressure to eat, be, and age in a healthy way, the group members used food as a means to collectively work towards their ideals of social justice. They have done so by developing digital skills and by adding their voice to the mediatized food culture. Therefore, they gained visibility and created socialization spaces. Through this project, they challenge ageist conceptions of older people’s (non) uses of technologies as means to improve their health or to keep in touch with their children and grandchildren. The research is based on the researchers’ participation in the project and is rooted in a cultural studies perspective, drawing from the literature emerging from the fields of critical food and ageing studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".