Bibliographic record
Abstract
Many older men do not cook or only participate in very basic food preparation, which may create challenges for those who might be required to prepare their own meals out of necessity in the future.By developing an understanding of older men's cooking experiences, designers may have the insights needed to develop design interventions to support this population with their cooking experiences.This preliminary study worked with two different participant groups, older men (ages 65 years and older) who do not cook or who only participate in very basic food preparation, and designers from product, service, and healthcare design fields.Three qualitative and exploratory design research methods were used: semi-structured interviews with the older men; journal-based cultural probe kits with the older men; and focus groups with designers using personas derived from the first two methods.From these methods' insights, an understanding into the perspectives of older men cooking was produced; including three primary themes relevant to design.Additionally, two recommendation categories, recommendations for design process and recommendations supporting cooking experiences, were created through insights produced for designers aiming to facilitate older men's cooking experiences through design intervention.iii
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".