Bibliographic record
Abstract
A growing, though still loosely connected, body of academic work has started placing meat at the centre of critical discourses regarding climate change and environmental sustainability, human health, economic wellbeing, food futures, and animal and ecological ethics. This special themed issue seeks to bring these multi-disciplinary scholars into direct conversation with one another under the umbrella of ‘Meat Studies’ as an emerging sub-field of study. Indeed, the recent establishment of Vegan Studies (see: Wright, 2015 and 2017) necessitates a parallel effort to better understand meat’s persistent social, economic, political, and cultural status in human societies. By situating meat at the centre of critical analysis, we identify, articulate, and address the challenges that meat poses in the twenty-first century. More generally, Meat Studies allows us to critically re-examine our cultural conventions regarding the ways in which we classify different foods, diets, identities, and culinary practices.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".