The Identity Myth: Constructing the Face in Technologies of Citizenship
Bibliographic record
Abstract
Over the last century, images of faces have become integral components of many institutional identification systems. A driver’s licence, a passport and often even a health care card, all usually feature prominently images representing the face of their bearer as part of the mechanism for linking real-world bodies to institutional records. Increasingly the production, distribution and inspection of these documents is becoming computer-mediated. As photo ID documents become ‘enhanced’ by computerization, the design challenges and compromises become increasingly coded in the hierarchy of gazes aimed at individual faces and their technologically mediated surrogates. In Western visual culture, representations of faces have been incorporated into identity documents since the 15th century when Renaissance portraits were first used to visually and legally establish the social and institutional positions of particular individuals. However, it was not until the 20th century that official identity documents and infrastructures began to include photographic representations of individual faces. This work explores photo ID documents within the context of “the face,”—a theoretical model for understanding relationships of power coded using representations of particular human faces as tokens of identity. “The face” is a product of mythology for linking ideas of stable identity with images of particular human beings. This thesis extends the panoptic model of the body and contributes to the understanding of changes posed by computerization to the norms of constructing institutional identity and interaction based on surrogates of faces. The exploration is guided by four key research questions: What is “the face”? How does it work? What are its origins (or mythologies)? And how is “the face” being transformed through digitization? To address these questions this thesis weaves ideas from theorists including Foucault, Deleuze and Lyon to explore the rise of “the face” as a strategy for governing, sorting, and classifying members of constituent populations. The work re-examines the techno-political value of captured faces as identity data and by tracing the cultural and techno-political genealogies tying faces to ideas of stable institutional identities this thesis demonstrates face-based identity practices are being improvised and reconfigured by computerization and why these practices are significant for understanding the changing norms of interaction between individuals and institutions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".