Theorizing Omission: State Strategies for Withholding Official Recognition of Personhood
Bibliographic record
Abstract
This article theorizes “omission,” which I define as the condition of being left out of administrative apparatuses, such as civil registers, censuses, and identity management systems. According to this theory, omission is not necessarily accidental but can constitute a political strategy. When even excluded statuses can be powerful grounds for claiming rights, resources, or membership, state actors can subvert such claims-making potential by depriving unwanted populations of the practical, material capacity to establish their legal personhood through documents and records. To situate omission, I develop a typology of documentary strategies additionally comprising “recognition,” “claims-making,” and “evasion.” Although my theorizing is informed by ethnographic research with unregistered families in Malaysia, scholars can apply this typology to multiperspectival, relational analyses of other empirical cases of documentary politics. Studying omissions has scholarly and ethical imperatives, not least to record the lives of populations denied, at times with existential consequences, the right to recognition.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".