On the Equivalence of Trolleys and Transplants: The Lack of Intrinsic Difference between ‘Collateral Damage’ and Intended Harm
Bibliographic record
Abstract
In this article I attempt to show conclusively that the apparent intrinsic difference between causing collateral damage and directly attacking innocents is an illusion. I show how eleven morally irrelevant alterations can transform an apparently permissible case of harming as a side-effect into an apparently impermissible case of harming as a means. The alterations are as obviously irrelevant as the victims’ skin colour, and consistently treating them as relevant would have unacceptable implications for choices between more and less harmful ways of securing greater goods. This shows not only how the principles philosophers have proposed for distinguishing between these cases cannot withstand scrutiny, but how we can be sure that there are no relevant differences yet to be discovered. I conclude by considering reasons to think that there are deontological constraints against harming, but that they apply just as forcefully against collateral harms as they do against intended harms.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.046 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".