Racial Exclusion by Bureaucratic Omission: Non-Enumeration, Documentary Dispossession, and the Rohingya Crisis in Myanmar
Bibliographic record
Abstract
Abstract This article traces the bureaucratic bases of the Rohingya crisis in Myanmar. I draw on ethnographic fieldwork and interviews with Rohingya activists to elucidate the political struggles and competing archival logics surrounding their disenfranchisement and displacement. I explain a curious shift in the past decade in Myanmar’s approach to managing the Rohingya population, whereby longstanding strategies of legally-encoded racial exclusion gave way to moves to withhold and contract the state’s administrative reach: 1) the repudiation of the Rohingya category in the 2014 census; and 2) the dispossession of documents leading up to the 2015 elections. I develop the concept of “bureaucratic omission” to reveal an alternative mode by which the state’s symbolic power can be accumulated and exercised. In the wake of new claims-making pressures during Myanmar’s short-lived democratic opening, state officials nullified Rohingyas’ claims for recognition as citizens by depriving them of the material evidence to support these claims. In response, Rohingya activists invoked this same epistemic power of documents, leveraging archival sources and documentary vestiges to build their own historical counternarratives of indigenous belonging. By protagonizing stateless Rohingyas, I provide insight into top-down administrative efforts to un-make race and into how minorities can contest these omissions.
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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 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".