Bibliographic record
Abstract
The International Criminal Court’s (ICC) legitimacy, as an independent and unbiased international criminal court, has been brought into question, for all 30 official cases opened to this date are against African nationals. The ICC-African relationship is often framed in this excessively simplistic dichotomy: either the ICC is regarded as a Western neo-imperial colonial tool, or as a legal institutional champion of global human rights, rid of the political. Nevertheless, each obfuscates the complexity of this relationship by purporting either extreme. Rather, it is the legal framework of the ICC that necessitates selectivity bias against nationals from developing countries, in particular, African states. The principle of complementarity and the United Nations Security Council’s (UNSC) referral power embedded in the ICC’s legal framework, allows for African nations to be disproportionately preliminarily examined, investigated, and then tried, while enabling warranted cases against nationals from developed states to circumvent such targeting. Therefore, the primary issue lies not in cases the ICC has opened, but in the cases it has not.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".