Identitk of vegan and vegetarian’s social representations and social eating practices through field study, traditional media and web2.0 user-generated content studies in four geo-cultural contexts
Bibliographic record
Abstract
The study presented in our empirical contribution benefits the interrelated world of education and health, due to the relevant applied implications of formal and informal education (in particular for the genesis and dissemination of social representations through web2.0 user-generated content among young people) as far it concerns new social eating practices, life-style and world’s views. In a world struck by climate change, economic crisis and increasing health issues, veganism and vegetarianism seem to be rapidly growing as phenomena proposing its alternative minority view on how to resolve some of these issues. The two notions can be always more frequently found in the centre of the public debates. The wide media coverage however leaves a divided public opinion, generating hegemonic, emancipated or polemical social representations depending on their degree of shared consensus. Inspired by Moscovici’s Social Representation Theory (1961/1976) [1] and his Active Minorities Theory (1976/1979) [2], the contribution investigates how groups of vegans and vegetarians see themselves as well as how are they being seen by the rest of population and comparing their views, attitudes and foods preferences in individual/social eating contexts in four different geo-cultural context/continents: Italy (Europe), Brazil (South America) and Canada (North America) in China (Asia). Three interrelated research lines inspired by de Rosa’s modelling approach [3] have been developed: a) field studies, administering de Rosa’s associative networks technique [4] [5] [6] and a specifically designed questionnaire (“Social Eating Practices”) to 484 participants, aged 19-55, belonging to 5 different self-declared groups of food-preferences (without food restrictions; meat- lovers; fish-lovers; vegetarians; vegans); b) traditional mass media, analysing on-line editions of the most read newspapers in three countries (Corriere della Sera –Italy, Correio Brazilense –Brazil, Toronto Star- Canada); c) Web2.0 social media analysis, investigating the topic related user-generated contents on Instagram and Twitter. The results indicate major similarities among vegan and vegetarian subjects in all countries, regarding the way they search for information and how they see themselves, but their views on life-style, food and social implications of their preference can be slightly different depending on cultural and economical reality of the country they live in. The level of tolerance of meat eater towards them can variate depending on country, as well as the mass-traditional or social media depiction characterized by top-down or bottom-up information structure: the former representing normative view related to the majority of food preference styles in the respective geo-cultural contexts, the latter as a channel for expressing ‘voices’ also of minority groups like vegans.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".