Bibliographic record
Abstract
A discussion of the state's “right to exclude” requires that we have at our disposal unambiguous understandings of what constitutes the state, who constitute its subjects/citizens, and who constitute aliens.1 This, however, is not the case. In fact, the reverse is more true: particular notions of the state, of subjects/citizens, and of aliens are the outcome of (among other things) practices of migration regulation. This essay interrogates two understandings of the state that characterize much scholarship on migration, including legal scholarship. First, the assumption of the salience of state borders, of citizens/subjects, and of aliens understood in national terms; second, the assumption—often condensed in invocations of the Westphalian state—that the authority to control migration across these putative borders is a longstanding and non-contentious element of state sovereignty. In such approaches, the state is simply there. It matters little what prefix—national, colonial, imperial, modern, and so forth—we affix to it. Such views are premised on the notion that the practices of governance and the institutions of the state have a fidelity to, can be deduced from, and simply reflect a set of principles. In my view, rather than understand the state as merely translating a set of principles into practice, we are better served by focusing on practices to examine how they interpret and remake principles in particular historical conjunctures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".