Subordinated Inclusion: Population Sorting Technologies as Digital Confinement in the United States, China, and Israel
Bibliographic record
Abstract
This article considers recent developments in population level watchlists that emerge from racialized economies. By demonstrating how they develop over time and in relation to population sorting technologies, it argues that these tools are increasingly used to include and control racialized groups in the same digitally enclosed space as protected citizens. This structured process stems from automation-assisted surveillance systems built by corporate state contractors such as BI Incorporated, HikVision, and Twentsche Kabel Holding Security Solutions. Building on the scholarship on the social life of surveillance and social sorting, it argues that automation assisted sorting of populations—asylum seekers in the US, Uyghurs in China, and Palestinians in Israeli controlled spaces—into categories of broadly defined color-coded threats produces population levels of control that deeply inhibit the autonomy of categorized individuals. Since threat production technology produces forms of automated racialization, by turning faces and behaviors into objects of data collection and analysis that are correlated to threat perception, it results in forms of subordinated inclusion that appear permanently foreclosed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".