Towards a broader understanding of citizenship in policy debate on food advertising to children
Bibliographic record
Abstract
Contemporary food policy often focuses on ‘downstream’ elements of the food system, particularly the roles and responsibilities of individual members of a consuming public. For example, to address the effects of food and beverage advertising on child health, in the absence of widespread agreement on the most appropriate form of collective action, policy debate has tended to revolve around moral reasoning about how children should behave and interact with the world around them. In this paper, we attempt to broaden this debate by sharing results from in-depth interviews (n=35) carried out as part of our Food Advertising to Children: Ethics for Policy study, funded by the Canadian Institutes of Health Research. We will discuss how ‘food citizenship’ can be viewed not only in terms of consumption roles (e.g. children’s behaviour), but the expectations for participation embedded within policy actor roles (e.g. health professionals, ‘government’ broadly defined). Such framing is important for how diverse policy actors understand and incorporate citizenship into their practices. Whether health professionals are construed as only ‘program delivery agents’, for example, or active citizens, can affect their power to influence policy processes; it also conditions the range of policy options deemed suitable for public debate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| 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".