Bibliographic record
Abstract
New food technologies are touted by some to be an indispensable part of the toolkit when it comes to feeding a growing population, especially when factoring in the growing appetite for animal products. To this end, technologies like genetically engineered (GE) animals and in vitro meat are currently in various stages of research and development, with proponents claiming a myriad of justificatory benefits. However, it is important to consider not only the technical attributes and promissory possibilities of these technologies, but also the worldviews that are being imported in turn, as well as the unanticipated social and environmental consequences that could result. In addition to critiquing dominant paradigms, the inclusive, intersectional ecofeminist perspective presented here offers a different way of thinking about new food technologies, with the aim of exposing inherent biases, rejecting a view of institutions like science and law as being objective, and advancing methods and rationales for a more explicitly ethical form of decision-making. Alternative and marginalized perspectives are especially valuable in this context, because careful reflection on the range of concerns implicated by new food technologies is necessary in order to better evaluate whether or not they can contribute to the building of a more sustainable and just food system for all.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".