Bibliographic record
Abstract
This article aims to explore the concept of victimless damage. This refers to paradoxical cases where a perpetrator and a moral wrong can be easily identified, but where somehow the role of the victim as such can be questioned. In order to explore this concept, I will first offer a typology of cases that could be labelled under this umbrella concept—namely, (1) cases of deceased victims, (2) biotechnological or no-identity cases, and (3) the ones related to lack of awareness due to epistemic injustice. Then, after highlighting the common traits and discussing some fuzzy cases, I will flesh out the main arguments for and against of the existence of and need for this concept, on the basis of both moral objectivism and subjectivism. In my view, delving into these arguments could shed some light on the metaethical debate on the sine qua non conditions of moral damage and its relation to moral wrongness. Finally, I will conclude by advocating for the need to introduce a gradational concept of moral damage and the second-person perspective into moral philosophy in order to take into account potential cases of victimless damage, but without having to accept the premises of moral realism.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.061 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".