Bibliographic record
Abstract
The Question In an innovative discussion Jason Scott Robert and Francoise Baylis (2003) argue that “the creation of novel beings that are part human and part nonhuman animal is sufciently threatening to the social order that for many this is a sufcient reason to prohibit any crossing of species boundaries involving human beings.” The “hypothesis” they propose is that “the issue at the heart of the matter is the threat of moral confusion.” They also argue that we cannot resolve this moral confusion by turning to science and appealing to the notion of species identity. This is because “there is no one authoritative denition of species.” Consequently, there is “no consensus on what exactly is being breached with the creation of interspecies beings.” So how are we supposed to go about answering this question about cross-species transgression? Curiously, this intriguing interdisciplinary discussion might say more about the intractable scale and complexity of bioethical inquiry in this domain than it does about the specic question it sets out to address. It boldly raises all the right issues in all their interdisciplinary splendor. But at the same time it invites the question how this multiplicity of interdisciplinary premises and arguments is supposed to lead to an answer. Bioethical discussions of this sort seem to promise answers, even if they do not provide them; they raise questions that are presumed to have answers. Yet this particular discussion left me wondering whether there really are answers of the expected sort to be had. In this commentary I explore this methodological worry by discussing two of the article’s central themes: species identity and moral confusion.
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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".