Bibliographic record
Abstract
Abstract This essay takes up two questions. First, what does it mean to say that someone creates her own luck? At least colloquially speaking, luck is conceived as something out of an agent's control. So how could an agent increase or decrease the likelihood that she'll be lucky? Building on some recent work on the metaphysics of luck, the essay argues that there is a sense in which agents can create their own luck because people with more skill tend to have more opportunities to benefit from luck. Second, what implications does this conception of luck have for related topics such as how we evaluate performances (like shooting an arrow), including coming to know something? The ubiquitous presence of luck in our actions is often underappreciated. The essay argues that when we combine an expected outcomes view of luck with a counterfactual view of causation, the distinction between environmental and intervening veritic luck seems to disappear. We need a more nuanced view of how luck sometimes undermines credit for success in agents' actions. The upshot of this view is that while luck may undermine the creditworthiness of an agent's success, it only partially undermines creditworthiness.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".