The Informationalization of Race: Communication Technologies and the Human Genome in the Digital Age
Bibliographic record
Abstract
This paper suggests that a new form of racialization is being produced in the information age through developments and innovations in communication technologies. Increasingly, racial knowledge is being constructed from seemingly neutral and unrelated pieces of information, which are collected, sorted, and analyzed through two key technologies: databases and the Internet. I call this interaction between technology and identity the "informationalization of race." As a mode of representation, a structuring device, and as a biological category, race is undergoing a significant transformation in the digital age. I ground this concept in a case study of the next Human Genome Project — the HapMap Project — to understand how technologies are being shaped in a specific institutional setting. Advances in human genomics have recently re-invigorated scientific research into the relationship between race and biology. Where the HGP concluded that humanity is similar at the genetic level, the HapMap Project began by looking for differences between white, African, and Asian groups. It's anticipated that promising findings from the HapMap project will be of help in developing pharmaceuticals that can target common diseases, such as cancer. However, this development also opens the door to old biological conceptions of race and a new phase of the biopolitics of the human body.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".