On Appeals to Nature and their Use in the Public Controversy over Genetically Modified Organisms
Bibliographic record
Abstract
In this paper I discuss appeals to nature, a particular kind of argument that has received little attention in argumentation theory. After a quick review of the existing literature, I focus on the use of such arguments in the public controversy over the acceptabil-ity of genetically-modified organisms in the food industry. Those who reject this biotechnology invoke its unnatural character. Such arguments have re-ceived attention in bioethics, where they have been analyzed by distinguishing different meanings that “nature” and “natural” might have. I argue that in many such appeals to nature the main deficiency of these arguments is semantic, in particular, that these words cannot be assigned a determi-nate meaning at all. In doing so, I rely on semantic externalism, a widely accepted theory of linguistic meaning.
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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".