Examining Nigeria’s Commitment to the International Criminal Court: A Response to Anya et al.
Bibliographic record
Abstract
Abstract The International Criminal Court (ICC) plays a key role in ensuring accountability for crimes under international law. Supporting this critical mission of the Court is vital to its survival. Understanding this, Anya et al. in a recent paper examined the relationships existing between states and the ICC. They determine that states can have a relationship of either credible commitment to support the work of the Court or sham devotion. Nigeria, Kenya and Uganda, were studied and based on their analysis of selected data, the writers make predictions for each state’s future engagement with the Court. While the study is compelling, I argue that the predictions made in the end, particularly in relation to Nigeria, are based on flawed data. I attempt to contextualize some of their assertions, particularly regarding immunity, complementarity and jurisdiction. This article ultimately calls for a more nuanced approach to evaluating state behavior towards the ICC, emphasizing the importance of contextual factors beyond quantitative measurements.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".