Bibliographic record
Abstract
Abstract This paper argues that there are two importantly distinct normative relations that can be referred to using phrases like ‘X is obligated to Y,’ ‘Y has a right against X,’ or ‘X wronged Y.’ When we say that I am obligated to you not to read your diary, one thing we might mean is that I am subject to a deontological constraint against reading your diary that gives me a non‐instrumental, agent‐relative reason not to do so, and which you are typically in a unique position to waive with consent. I call this first relation the constraint relation . A second thing we might mean is that you are in a position to fittingly hold me personally accountable for reading your diary by demanding that I not read your diary, resenting me if I do so without excuse, and deciding whether to forgive me for this afterwards. I call this second relation the accountability relation . Though these two kinds of directed obligation often coincide, I argue that they are extensionally dissociable and play different normative roles. We cannot provide an adequate theory of ‘obligation to ’ until we recognize that this phrase denotes not one relation, but two.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".