Assembling noncitizenship through the work of conditionality
Bibliographic record
Abstract
We develop a framework for understanding noncitizenship that combines attention to systemic processes with interest in contingency and indeterminacy in the production and substantive practices associated with noncitizen legal status categories and trajectories. We argue that noncitizenship is a dynamic, multi-scalar assemblage that brings together disparate elements in patterned and changing ways. Individuals and institutions generate the formal and substantive systems that confer or deny noncitizens the formal and substantive right to be present in a country and/or to access entitlements. Noncitizens exercise agency in choosing to make claims (or choosing to not make claims) to substantive rights, and the individuals and institutions with which they interact may facilitate or hinder such claims-making. In this process, social actors are enacting conditionality; they are working to meet the conditions required to maintain presence and access. Discretion, migrant agency, unequal social interactions, and social learning unfold over time and can generate a range of experiences of noncitizenship and legal status trajectories. These do not necessarily conform to expected pathways and timelines, and may combine access to various resources and public goods in variable and contingent ways. We illustrate the framework with data from research conducted in Toronto.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".