Shoshana Amielle Magnet, When Biometrics Fail: Gender, Race, and the Technology of Identity
Bibliographic record
Abstract
When the twin towers fell in 2001, the American government began a “war on terror” on a number of fronts. Some we could see, and others we could not. The Bush administration focused its primary military efforts in various parts of the world to combat and disrupt groups identified as “terrorists.” A second front focused on protecting the home soil by identifying and investigating potential threats. The American government deployed probably the most visible part of this strategy in airports where the practice of taking a domestic flight changed almost overnight. Prior to 9/11, one could walk their family member right up to the departure gate to say good-bye. That practiced completely changed and the state set up security screening in every airport to check people and their carry-on items, just like in international departures. Many everyday items could be potential threats and the TSA agents screened everything with scrutiny, even our shoes. The Bush government also implemented another front in the war on terror in digital space that is much more difficult to see. Aided by the Patriot Act, the intelligence agencies developed and deployed many new information technologies for identifying risks and individuals and monitoring groups. These surveillance technologies included linking criminal databases at every level (local, state, national, international), monitoring cell phone and e-mail traffic, checking individual’s library records, full pipe Internet surveillance, digital fingerprinting, retina recognition, and other biometric technologies for capturing human bodies and turning them into digital data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".