Diet for a Better World: Exploring the Intersectional Impact of Meat-based vs Plant-Based Diets and First Steps for Change
Bibliographic record
Abstract
Humanity is faced with numerous pressing issues from the health crisis and human rights violations to climate change and animal cruelty. Although various methods are being employed to solve these issues, dietary changes are often overlooked. Unbeknownst to many people, the meat, dairy and fish industries continue to play a substantial role in perpetuating environmental degradation, animal cruelty, and human rights issues. However, the adoption of a 'plant-based' or 'vegan' diet emerges as a powerful catalyst for yielding widespread change in these issues. This research paper aims to explore the importance of transitioning society away from animal-based diets and towards plant based meals. Addressing a spectrum of urgent issues, this paper underscores the often underestimated potential of transitioning to plant-based diets as a potential solution to human, animal and environmental issues. The paper commences with a meta-analysis, employing an intersectional lens of human ethics, animal ethics, environmental concerns, and health perspectives, to evaluate the negative repercussions of animal agriculture industries. Perceived negatives of plant-based diets will then be explored to holistically conceptualize whether veganism is a valid and feasible option for individual and societal change. Lastly, drawing from prior studies and acknowledging the barriers of transitioning to a plant-based lifestyle, the paper culminates in proposing first steps for creating a successful plant-based transition: the implementation of plant-based meals and education in schools.
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 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 | 0.041 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".