Precarious legal status trajectories as method, and the work of legal status
Bibliographic record
Abstract
Critiques of ‘fantasy citizenship’ include calls to migrantize the citizen and denationalize citizenship and migration studies. In response, this essay proposes ‘precarious legal status trajectories (PLSTs) as method’, with a focus on the work of legal status. This approach captures changes in sociolegal status trajectories, including illegalization, and builds a ‘thicker’ approach to trajectories. The work of status refers to effort, time, money, and other resources devoted to being present in a jurisdiction, and/or gain access to services and protections. The approach also considers work that does not produce changes and is not counted, and interactions with other actors. This contributes to understanding how precarious legal status trajectories are assembled and contribute to inequalities in citizenship and dynamics of differential inclusion. It migrantizes the citizen in a context where the share of citizens who were precarious noncitizens continues to rise, and when methodological nationalism occludes PLSTs.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.081 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".