Bibliographic record
Abstract
I am very grateful to Simon Beck for his thoughtful response to my paper “Transplanting Brains?” (2016). Needless to say, he raises more issues than I can hope to answer in a brief response. While Beck seemingly feels that the deck has been stacked against him, I think that the majority of his criticisms result from misconceptions and misunderstandings that I intend to straighten out in what follows. Before proceeding, I would like to draw attention to a worry that is lurking in the shadows. Perhaps Beck and I talk at cross purposes. While Beck is concerned with a metaphysical theory of personal identity that supposedly holds across all possible worlds—and as such places heavy importance on counterfactuals and intuitions—I am concerned only with the natural world with the aim of generating empirically substantiated hypotheses about how things really are when it comes to persons persisting through time. Now, here is a disclaimer: If the natural world does not exhaust reality, then my discussion is only partial. It goes without saying that most contemporary philosophers given a choice between going with science and going with intuitions, go with science.
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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.027 | 0.067 |
| Insufficient payload (model declined to judge) | 0.004 | 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".