Bibliographic record
Abstract
Abstract Taking into account what we have already learned so far from Third World Approaches to International Law (TWAIL) scholars, Indigenous scholars, and other critical race scholars, in this short chapter we try to unpack the meaning and scope of race and ethnicity, through our own standpoints. First, we provide a critical overview of the race and ethnicity scholarship, paying close attention to the commentary of a few key interlocutors for our project in the short space of this chapter in the much larger project of this handbook. Next, we examine the place of race, and its displacement by ethnicity, in international law and regional human rights instruments. Tracking the social and scholarly move from biological determinism to social construction of what these concepts signify, we also assess the pragmatic and ideological reasons for a parallel ambiguity of these terms in international and human rights law. Ultimately, following our key interlocutors, we see this lack of definition and displacement of race as a tactic in the larger project of splitting solidarities and resetting the uneasy routes to more radical worldmaking. We conclude by briefly discussing two cases that show the pitfalls of juridification and the sometimes unintended and unsolicited transformations wrought by “ethnoracial” litigation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".