Food for thought: SDG challenges, corporate social responsibility and food shopping in later life
Bibliographic record
Abstract
With the context of changing global and local populations and, for example, their composition and distribution, this paper offers insight to food shopping in later life with a focus on Nottingham and Nottinghamshire in the East Midlands. The work is relevant and important due to the specific population makeup of this area and the challenges in achieving the UN Sustainable Development Goals (SDGs) as a result of population changes/challenges. The work takes an interdisciplinary view and draws on literature from both social policy and social care and business and marketing. Using this work as a grounding, and insights to primary research from a wider study in this area, the paper offers discussion and comment on: the importance of food and food shopping in later life; issues of, and concerns for, health, well-being, identity and community maintenance and resilience (as a direct result of the challenge to SDG achievement); and the role(s) and responsibility of business from a core business and wider business/corporate responsibility perspective as a reflection of the above and findings of the work. Using primary research undertaken by the authors, the paper supports findings from existing work from across social policy and care and business and management – related to the practicalities, challenges and the role of and approaches to food shopping in later life. It specifically offers insight to the efforts made by older food shoppers to maintain their independence and support their choices in a context of interdependence (e.g. within a family, community and environment). The importance social aspects of food shopping (as a counter to isolation and loneliness for example) are also identified and how, for example, the actions of business(es) may undermine the efforts (and resilience) of individuals and communities. “Better” understanding of food shoppers by business and other stakeholders is promoted.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".