Street‐Level Bureaucrats Manufacturing Migrants: An Implementation Study of Policy Measures to Address Statelessness in the Dominican Republic
Bibliographic record
Abstract
ABSTRACT Migration policy implementation studies based on Western European or North American contexts may assume that those affected by a given policy are indeed migrants. In developing country contexts, where undocumented nationals can be indistinguishable from the foreign‐born, implementation enables street‐level bureaucrats to manufacture migrants out of those deemed not to belong through administrative manoeuvres. The Dominican Republic provides a critical instance of how street‐level bureaucrats can make migrants out of people who never crossed an international border, with devastating impacts on their access to social rights. Most scholarship on the case comes from international law and investigates the 2013 Constitutional Court sentence which retroactively stripped citizenship from an estimated 133,770 descendants of Haitian migrants born in country dating back to 1929. Less scholarly attention has examined the implementation of subsequent policy measures adopted by the Dominican state—regularisation for migrants, naturalisation for their descendants. Conceptually, this paper situates itself within critical policy studies, combining insights from the bottom‐up literature on implementation studies in the “Global South” and critical analysis of policy implementation. Drawing on a case study complemented by ethnographic observations, the paper argues that in developing country contexts characterised by “intentional ambiguity” (Frost), policy implementation can perpetuate the very problem the policy purports to address. Intentionally ambiguous implementation can serve as a state strategy for signalling a policy change to the international community, whilst the reality on the ground remains unchanged.
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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 | 0.009 | 0.009 |
| 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 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".