Distinguishing internal property from external property in kidney transplantation
Bibliographic record
Abstract
What determines the ownership of human body parts? In this paper, I argue that this question can be informed by an exploration of the cognitive distinction between property external to the human body such as houses, cars or land, and internal property such as organs that are located within anatomical body confines. Each type of property has distinct brain representations and possibly different effects on the sense of self. This distinction may help explain the divergence in post-donation outcomes seen in different kidney donor populations. Poor outcomes in some types of kidney donors may be due not only to a failure in their proper selection by standard medical testing or post-donation care but may also be a manifestation of differing effects on sense of self resulting from transfer of their internal property. Because a kidney is internal property, a hypothesis worth exploring is that those who experience good outcomes post-donation experience dopaminergic activation and a feeling of reward, while those experiencing bad outcomes are instead overcoming cortisol or adrenergic-based stress or fear responses without a corresponding feeling of reward, disrupting of their sense of self. Discussions about the rules for internal property transfer must be based not only on values and laws designed to govern external property but also on cognitive science-based facts, values and judgments that discussions of external property do not presently accommodate. Any future system of rules for governing organ distribution requires a framework different from that of external property to prevent harm to living kidney donors.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".