Bibliographic record
Abstract
What is law as minor jurisprudence? Existing scholarship around the concept of minor jurisprudence associates it with processes that start something, found something, or stand outside something and, further, with processes that conceal or erase. To seek to clarify the meaning and effect of the phrase ‘“law as” … minor jurisprudence’, I consider cases of mistake that, as legal speech acts, transform a state of affairs. In Part I, I use JL Austin’s theory about speech acts to analyse how speech can be mistaken and how such mistakes may transform states of affairs, using cases of encounters between Indigenous, settler, and international legal orders. In Part II, I survey the debut of the Trump administration as a possible site of minor jurisprudence. Informed by this study of mistakes that help make minor jurisprudence, my investigation suggests that: 1) minor jurisprudence requires major jurisprudence to be cognizable; 2) little intrinsic to the concept of minor jurisprudence offers normative guidance; and 3) the idea of law as minor jurisprudence may be a mistake. If ‘law as’ is understood as a practice of legal knowledge, what matters is the process of drawing the line between major and minor, not the minor itself.
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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.100 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| 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".