Bibliographic record
Abstract
Abstract This article suggests an account in the language of criminal law that merits the language for criminal accountability over the language of human rights, as a form of accountability, when prosecution is not possible. Calling for the prosecution of those most responsible for international crimes seems to be feasible after the war has ended, or at least when there is a vision for a political transition, but the war in Syria is ongoing and a vision for political transition remains elusive. The Syrian conflict has produced almost all kinds of heinous crimes, yet there is no clear political will to hold the alleged perpetrators of atrocity crimes accountable. At the same time, calls for criminal accountability in Syria, and discourse to achieve international criminal justice are taking place before the civil war ends. This article relies on the expressive theory of punishment to assess the rationales of calls for criminal accountability during the ongoing conflict in Syria. Out of many rationales, the article notes that calls for criminal accountability open the possibility of punishment and send a message of condemnation to perpetrators as well as a message of acknowledgment to victims. Furthermore, using the language of criminal accountability as a basis for the calls is stronger than using the language of human rights. The article discusses the problem of standing to call those responsible for international crimes to account and proposes that our shared humanity provides the authority for such calls while also pointing out limitations of this approach.
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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.060 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.013 | 0.015 |
| 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".