Bibliographic record
Abstract
In this paper, I argue that principled criminalization does not have to rely on critical objectivity. It is not necessary to demonstrate that conduct is criminalizable only if it is wrong in a transcultural and truly correct sense. I argue that such standards are impossible to identify and that a sounder basis for criminalization decisions can be found by drawing on our deep conventional understandings of wrong. I argue that Feinberg’s harm principle can be supported with conventional accounts of harm, and that such harms can be identified as objectively harmful when measured against our deep conventional understandings of harm. The distinction that critical moralists make between truly harmful conduct and conventionally objective harmful conduct is unsustainable because many conventional harms impact real victims in social contexts. The best that we can do is to scrutinize our conventional conceptualizations of harm and badness, but that scrutiny is constrained by the limits of epistemological inquiry and our capacity for rationality at any given point in time. Many acts are criminalizable because they violate social conventions that are shareable by communally situated agents.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".