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Record W7065896244

Exploration of Meat Reducers’ Motivations and Their Food Choices

2022· dissertation· en· W7065896244 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedissertation
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsnot available
Fundersnot available
KeywordsOmnivoreConsumption (sociology)Feeding behaviorFood choiceSample (material)Meat packing industryBehaviour change
DOInot available

Abstract

fetched live from OpenAlex

The past decade has observed an increasing number of consumers who are reducing their meat consumption without giving up meat altogether, known as meat reducers or flexitarians. However, relatively little is known about meat reducers in Canada. The goal of this study is to understand the motivations, perceived barriers, and current practices of meat reducers, compare them with other diet groups, and explore heterogeneity among them. To achieve this, we conducted an online survey with students attending a Canadian university (n=438). Our sample consisted of regular omnivores, omnivores reducing meat, meat reducers and vegetarians/ vegans. Results revealed that meat reducers are a distinct diet group that fall between regular omnivores and vegetarians/vegans on a broad range of variables. Lastly, we conducted latent profile analysis and identified four segments of meat reducers: ethical reducers (21%), mixed-motive reducers (24%), uncommitted reducers (41%), and restrictive reducers (14%).

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.210
Teacher spread0.190 · 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