Flickering Presence: Theorizing Race and Racism in the Governmentality of Borders and Migration
Bibliographic record
Abstract
Analytics of biopolitics and government have proven to be powerful tools in a growing scholarship examining the bordering, surveillance, securitization and contestation of migratory processes. Yet the critical potential of such research is hampered by the rather limited ways it has managed to make sense of race and racism. While Foucault was insistent that governmentality should orient itself to the understanding of singularities, too often race appears, when treated at all, as a general phenomenon. This article makes two contributions aimed at addressing these shortcomings. First, we survey studies in the governmentality of migration and develop a typology of what we call framings of race – the ways that race appears, is mobilized, or haunts this scholarship. Second, we look to recent debates about race and racism in Science & Technology Studies for useful theoretical innovations that might help us study border- and race-making as mutually constitutive processes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".