Review of: "Beyond the Luck Problem: Addressing Discrimination in Event-Causal Libertarianism"
Bibliographic record
Abstract
Potential competing interests: No potential competing interests to declare.In this paper, Maria Sekatskaya provides a discrimination problem with event causal libertarian free will.She imagines Carol, whose decisions have always been realized by deterministic brain processes, so she is not responsible for them.She then imagines Linda, whose decisions are realized by indeterministic brain processes, so she is responsible for them.Carol and Linda are phenomenologically indistinguishable, so both are equally praised/blamed by themselves and others for their actions, but only Linda is actually responsible.Fortunately, portable neural scanners are invented to discern who is actually responsible.Sekatskaya argues that this thought experiment leads to two unpalatable options.First, we can treat Linda and Carol unequally, by holding Linda responsible but not Carol.But it seems unjust to discriminate on the
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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 | 0.026 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.050 | 0.026 |
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".