Normative Uncertainty Without Unjustified Value Comparison: A Response to Carr
Bibliographic record
Abstract
Jennifer Rose Carr’s article “Normative Uncertainty without Theories” proposes a method to maximize expected value under normative uncertainty without Intertheoretic Value Comparison (hereafter IVC). Carr argues that this method avoids IVC because it avoids theories: the agent’s credence is distributed among normative hypotheses of a particular type, which don’t constitute theories. However, I argue that Carr’s method doesn’t avoid or help to solve what I consider as the justificatory problem of IVC, which isn’t specific to comparing theories as such. This threatens the implementability of Carr’s method. Fortunately, I also show how Carr’s method can nevertheless be implemented. I identify a type of epistemic state where the justificatory problem of IVC is not a necessary obstacle to maximizing expected value. In such states, the uncertainty stems from indecisive normative intuitions, and the agent justifiably constructs all the normative hypotheses (each on the basis of a different, internally-consistent subset of her normative intuitions) by reference to the same unit of value. This part of my argument complements not only Carr’s argument, but also some moderate defenses of explicit IVC. The combination of Carr’s paper and mine helps to illuminate the conditions for maximizing expected value under normative uncertainty without unjustified value comparison.
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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 | 0.030 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".