Creating a Mind Genomics Wiki for Non-Meat Analogs
Bibliographic record
Abstract
In the past few decades, several negative aspects of excess meat consumption have been identified, ranging broadly from health to environment to consumer rejections of meat analogs. At the same time, however, several new meat alternatives have emerged such as algae, insects, and cultured meat, which all present a sustainable option to reduce meat consumption. The paper assesses the psychology of the “everyday” for meat-free products, focusing on how consumers in two specific markets in the USA (California, New York) respond to messages about four specific topics involving meat-free products. These four are sensory characteristics, possible usage in products, health aspects, and environmental aspects, respectively. Each study with 100 or more respondents used experimental design of messages (Mind Genomics) to understand the degree to which the respondents reacted positively or negatively to the 16 messages in each of the four studies. The data suggest that focusing on the Total Panel or on geography, gender, or age will not reveal the dramatically different mind-sets existing in each of the four topics. We introduce the notion of the PVI, personal viewpoint identifier, to help the researcher uncover these mind-sets, and help communicate effectively with each mind-set about meat analogs or help recruit these individuals to participate in further studies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".