Further Reflections on Lemos’s Indeterministic Weightings Model of Libertarian Free Action
Bibliographic record
Abstract
John Lemos defends an indeterministic weightings model of libertarian free will that is a variant of event-causal libertarian views. Many argue that these views are susceptible to the luck problem: an agent’s directly free choices are too luck infected for the agent to be morally responsible for them. The weightings model supposedly escapes this problem largely because in this model an agent’s reasons for choices do not come with pre-established values. Rather, an agent performs intentional acts of weighting that contribute to the value she assigns to her reasons. Decisions that are consequences of weightings are, thus, under the agent’s control and not subject to luck. In a recent paper, I argued that despite its weighting component, Lemos’s model succumbs to the luck problem. Lemos rejoins that my criticisms are based on misunderstandings and confusions. I deflect the charge of misperception and explain why the weightings model remains susceptible to the luck problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".