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Record W4255999322 · doi:10.1080/02580136.2015.1106705

Transplanting brains?

2016· article· en· W4255999322 on OpenAlexafffund
Nils‐Frederic Wagner

Bibliographic record

VenueSouth African Journal of Philosophy · 2016
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsCarleton University
FundersUniversity of Ottawa
KeywordsTransplantingPhilosophySociologyHistoryBiologyBotany

Abstract

fetched live from OpenAlex

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’.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.279
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes2
Has abstractyes

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