From incitement to indictment? Prosecuting Iran's president for advocating Israel's destruction and piecing together incitement law's emerging analytical framework
Bibliographic record
Abstract
On October 25, 2005, at an anti-Zionism conference in Tehran, Iran's president, Mahmoud Ahmadinejad, called for Israel to 'be wiped off the face of the map' – the first in a series of incendiary speeches arguably advocating liquidation of the Jewish state. Certain commentators contend that these statements constitute direct and public incitement to commit genocide. This Article analyzes this assertion by examining the nature and scope of recent groundbreaking developments in incitement law arising from the Rwandan genocide prosecutions. It pieces together an analytical framework based on principles derived from these cases, including the Canadian Supreme Court's opinion in the Leon Mugesera matter. Using this framework, this Article demonstrates that while a successful prosecution would entail clearing significant substantive and procedural hurdles, it could include both incitement and crimes against humanity charges in light of the incitement's nexus with Iran's sponsorship of terrorist attacks against Israel. However, the International Criminal Court would have to put aside political pressures related to the Middle East's toxic political environment and the absence of causation, and agree to take the case. Given incitement law's track record to date, with prosecutions occurring only post-genocide, the odds against such a prosecution are long. As a result, the Article proposes that incitement law shift its focus from punishment to deterrence. This would permit early intervention and center incitement on its core mission of atrocity prevention. This Article also suggests that euphemisms employed to disguise incitement, such as 'predictions' of destruction, when anchored to direct calls for violence, should also be considered acts of direct incitement. Finally, with respect to crimes against humanity, the Article explains that attacks on a civilian population carried out by a proxy at the insistence of the inciter, rather than directly by the actual inciter himself, should be sufficient to establish liability. At the same time, in the interest of protecting free speech, the crime should not be charged absent evidence of calls for protected-group violence, as opposed to mere hatred.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| 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".