TOWARDS BUREAUCRATIZATION: AN ANALYSIS OF COMMON LEGAL REPRESENTATION PRACTICES BEFORE THE INTERNATIONAL CRIMINAL COURT
Bibliographic record
Abstract
The status of victims in the international criminal project since the establishment of the International Criminal Court (ICC) is largely dealt with in the literature. Article 68(3) innovated as it allows victims of mass crimes to present their views and concerns before the first permanent international criminal jurisdiction. Yet, the case law over the last two decades shows that victims will not have the opportunity to directly take part in the ICC proceedings. Those who will participate to the trials are rather their legal representatives. This article explores victim participation through this new actor of international criminal trials. Common legal representation – i.e. the representation of hundreds, or even thousands, of victims by a sole lawyer – was promptly presented as unavoidable. To ensure operational efficiency, the selection of the legal representative was institutionalized. The practice of the Court prompted some impersonal lawyer-clients relationship. The author demonstrates the similarities between the organization of common legal representation in the ICC and the ideal-type of bureaucracy imagined by Weber.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".