Bibliographic record
Abstract
Abstract There is a widely accepted distinction between being directly responsible for a wrongdoing versus being somehow indirectly or vicariously responsible for the wrongdoing of another person or collective. Often this is couched in analyses of complicity, and complicity's role in the relationship between individual and collective wrongdoing. Complicity is important because, inter alia, it allows us to make sense of individuals who may be blameless or blameworthy to a relatively low degree for their immediate conduct, but are nevertheless blameworthy to a higher degree for their implication in some larger (or another person's) wrongdoing. In this paper, I argue that there is a distinctively epistemic kind of complicity. First, I motivate the distinction between direct and vicarious responsibility with three interlocking arguments, respectively appealing to: (i) the structure of rational agency; (ii) linguistic considerations; (iii) the role of ‘principal' vs. ‘accomplice’ in legal doctrine. I show how these arguments naturally extend to the epistemic domain, motivating an epistemic form of vicarious responsibility. I then examine complicity as a mechanism of vicarious epistemic responsibility. To fill this out, I engage with an epistemic analogue of the debate about the role of intention versus causal contribution in complicity. I propose a Casual Account of Epistemic Complicity, arguing that it accommodates a wide range of cases, and enables fine-grained explanations of degrees of culpability for epistemic complicity. With an adequate account of epistemic complicity on hand, we can explain what is objectionable about an important class of epistemic agent who, on an individual level, may be epistemically blameless or blameworthy to a relatively low degree, but whose relation to other individuals or collectives nevertheless makes them epistemically blameworthy to a higher degree. I explore some broader implications of this result.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".