Bibliographic record
Abstract
The concept of “legitimacy” can foster new insights and be of wide relevance to migration research: states seek to exercise “legitimate” power to regulate movement, organizations strive for “legitimacy” in their fields, and individuals want to occupy “legitimate” positions. The concept's usage, however, is largely isolated to specific contexts and cases. Those looking to engage it in their work face at least two challenges: (1) existing studies understand and apply legitimacy in different ways; and (2) there are no well-defined theoretical perspectives in the field to draw on. To facilitate such engagement, I first propose some shared understandings of legitimacy—namely a definition and conceptualization—that are widely applicable, amenable to diverse perspectives and approaches, and analytically useful. I then outline a theoretical perspective—one relating legitimacy to the actions of key players in migration (individuals, groups, organizations, states, etc.)—that can help researchers integrate legitimacy into their work, seek new avenues for future study, and bring the concept into wider conversations in the field. Finally, I illustrate how these arguments can enable new insights by expanding first on the theory of functional imperatives, followed by three substantive areas of migration research: the implementation and effectiveness of migration policies; the migration state and federalism; and status, deservingness, and social movements.
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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 | 0.040 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.007 | 0.095 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| 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".