The International Criminal Court and the Justice Cascade
Bibliographic record
Abstract
Abstract We present an original interpretation of the justice cascade theory developed by Kathryn Sikkink and her coauthors as it pertains to the icc ’s engagements with African states since 2004. In doing so, we challenge a prominent and acclaimed critique of this theory: Oumar Ba’s States of Justice . Ba presents four qualitative case studies informed by fieldwork, focused on the admissibility challenges, selective cooperation, and obstructionism involving Uganda, Libya, Kenya, and Côte d’Ivoire. We closely examine the key publications in which the justice cascade theory is introduced, refined, and critiqued, identifying misinterpretations of this theory in Ba’s work and elucidating its empirical implications. Furthermore, we perform a citation analysis of States of Justice , demonstrating that the book, by virtually omitting primary sources of any type, misimplements its own empirical strategy. We introduce fresh legal analyses of compliance with the Rome Statute of the icc in the four relevant cases, revealing the dearth of evidence of noncompliance in all but the Kenyan case. Finally, we discuss legal analysis as a means of testing theories of international law and courts, and we illustrate the relevance of the justice cascade theory to current debates on the establishment of new international tribunals.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".