Lawfare: Lawfare and War Crimes Tribunals (Panel 3) (Part 4)
Bibliographic record
Abstract
September 10, 2010 War Crimes Research Symposium Frederick K. Cox International Law Center Case Western Reserve University School of Law Moderator: Prof. Michael Kelly Speakers: Hon. James Ogoola, Principal Judge, Ugandan High Court Robert Petit, former International Prosecutor, Cambodia Tribunal, Counsel, War Crimes Section, Federal Department of Justice, Canada Prof. David Crane, founding Prosecutor, Special Court for Sierra Leone, Syracuse University College of Law Prof. Jens Meierhenrich, London School of Economics & Political Science, author, Lawfare: The Formation and Deformation of Gacaca Jurisdictions in Rwanda Amb. David Scheffer, Northwestern University School of Law, former U.S. Ambassador at Large for War Crimes Issues Summary: Traditionally "Lawfare" was defined as "a strategy of using—or misusing—law as a substitute for traditional military means to achieve an operational objective." But lately, commentators and governments have applied the concept to International Criminal Tribunals, the defense counsel's tactics challenging the detention of al Qaeda suspects in Guantanamo Bay, and as indicated in the quote above to the controversial Goldstone Commission Report. This symposium and Experts Meeting, featuring two-dozen leading academics, practitioners, and former government officials from all sides of the political spectrum, will examine the usefulness and appropriate application of the "Lawfare" concept.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.117 | 0.032 |
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".