Bibliographic record
Abstract
In The Social Life of Biometrics, biometrics is loosely defined as a discrete technology of identification that associates physical features with a legal identity. Author George Grinnell considers the social and cultural life of biometrics by examining what it is asked to do, imagined to do, and its intended and unintended effects. As a human-focused account of technology, the book contends that biometrics needs to be understood as a mode of thought that informs how we live and understand one another; it is not simply a neutral technology of identification. Placing our biometric present in historical and cultural perspective, The Social Life of Biometrics examines a range of human experiences of biometrics. It features individual stories from locations as diverse as Turkey, Canada, Qatar, Six Nations territory in New York State, Iraq, the skies above New York City, a university campus and Nairobi to give cultural accounts of identification and look at the ongoing legacies of our biometric ambitions. It ends by considering the ethics surrounding biometrics and human identity, migration, movement, strangers, borders, and the nature of the body and its coherence. How has biometric thought structured ideas about borders, race, covered faces, migration, territory, citizenship, and international responsibility? What might happen if identity was less defined by the question of “who’s there?” and much more by the question “how do you live?”
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".