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Towards a broader understanding of citizenship in policy debate on food advertising to children

2013· book-chapter· en· W48598464 on OpenAlexaffabout
Catherine L. Mah, Brian Cook, Sylvia Hoang, Emily Taylor

Bibliographic record

VenueWageningen Academic Publishers eBooks · 2013
Typebook-chapter
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsCentre for Addiction and Mental HealthToronto Public HealthUniversity of Toronto
Fundersnot available
KeywordsFraming (construction)CitizenshipPublic relationsPublic policyPolitical scienceFood policyGovernment (linguistics)EngineeringLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.316
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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