Bibliographic record
Abstract
Brain transplant thought experiments figure prominently in the debate on personal identity. Such hypotheticals are usually taken to provide support for psychological continuity theories. This standard interpretation has recently been challenged by Marya Schechtman. Simon Beck argues that Schechtman's critique rests upon ‘two costly mistakes’—claiming that (1) when evaluating these cases, philosophers mistakenly try to figure out the intuitions that they think people inhabiting such a possible world ought to have, instead of pondering their own intuitions. Beck further asserts that (2) brain transplant thought experiments cannot confirm any given theory of personal identity but rather they can only rule out theories. I argue on grounds of the social ontology of personhood that Beck has things back to front. Since our concept of personhood is shaped and informed by contingent de facto norms and structures of the natural world, and as such is heavily normatively laden, the conceptual genesis of personhood must be taken into account. This calls for constructing thought experiments as realistically as possible in order to trigger reliable intuitions. Furthermore, drawing on recent evidence from cognitive science, an empirically informed look at brain transplant thought experiments considering ‘Embodied Cognition’ reveals that Beck's arguments not only fall short for supporting psychological continuity theories, but also suggests an advantage of Schechtman's ‘Person Life View’.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".