History and contestation: On teaching <i>Diversity and Self-Determination in International Law</i>
Bibliographic record
Abstract
In this article, I share some thoughts about the afterlives of Karen Knop’s work within the space of the classroom, a space she inhabited with humility, interest, and care. I am approaching this as an ode to her, not only as a brilliant scholar but also as a wonderful teacher, supervisor, and mentor. Every year, I teach ‘The Canon of Self-Determination,’ a chapter from Knop’s 2002 book Diversity and Self-Determination in International Law. Through this chapter, students learn about the trial as a space of political contestation and of rupture and continuity and as a space that opens a conversation with the past as it is constantly shaped and reshaped by the present. Knop was concerned with the question of decolonization in international law and how, through the courtroom, anti-colonial lawyers put international law itself on trial. In this article, I want to think about the enduring significance of teaching this text every year.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.073 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".