Responding to neoliberal diets: School meal programmes in Brazil and Canada
Bibliographic record
Abstract
Introduction Food is a tool of power that has permitted capitalismto be established as the dominant mode of production(McMichael, 2001). Within capitalism, cheap foodfrom the ‘Green Revolution’ sought to prevent the‘Red Revolution’ (Patel, 2013), or so-called‘communist threat’. Even though it is recognised asa fundamental human right (UN, 2013), food isincreasingly mercantilised as a commodity (Murphy,2009). This term refers to the perversion of foodfrom a social good into merchandise, produced andsold as any other. As this volume demonstrates, thisprocess in connected with many harms associated withfood and agriculture (Gray and Hinch, 2015). Although for some time global agriculture has producedenough for every person in the world to have accessto 2,850 calories – enough to live healthily (TheWorld Bank, 2008) – about 795 million people arestill subject to hunger (FAO, 2015). Concurrently,the World Health Organization (WHO, 2016) indicatesthat more than 1.9 billion people are overweight dueto unbalanced diets. This is not simply because theyeat too much, but because they consumeindustrialised foods of low nutritional value(Guthman, 2011). Children and youth are among the most vulnerable to theconsequences of such discordance in the agri-foodsystem (Gunson et al, 2016). For instance, in manypoor countries malnutrition starts in utero when amother's precarious nutritional intake affects theformation of a foetus's biological structures(Ziegler, 2011). Additionally, due to a powerfulprocess of ‘conquering minds’ through advertising,millions are persuaded to partake in a low-nutrientdiet model, exemplified by fast foods (Azuma andFisher, 2001; Schlosser, 2001; Nestle, 2002; Pollan,2007). Hunger and obesity are not episodicmanifestations; on the contrary, they havebiological, economic and social determinations, tothe extent where one can even inherit its detriment.This chapter discusses the standardisation of dietsas the result of neoliberal capitalism, as well asthe forms of resistance and countermeasures inschools. The school-aged demographic of Western countries havelargely adopted food habits based primarily onprocessed goods of low nutritional value (Winson,2010). Changes in school meal programmes are seen asa form of reverting this process (Poppendieck,2010).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".