Bibliographic record
Abstract
Modern liberal citizenship is a failing design, and this is nowhere more apparent than in the contemporary US. Currently there is a frenzy around US citizenship – who has it but shouldn't have it, who should have it but doesn't have it, who had it but renounced it. The sheer volume of ideas, images, and events and their mass circulation makes it almost impossible not to notice how unsettled and unsettling contemporary US citizenship has become. If, as designer Bruce Mau suggests, the success of a design is its invisibility, then it seems that the design of contemporary US citizenship is anything but a success. Taking seriously the claim that modern liberal citizenship is a failing design, this article focuses on how citizenship is designed and redesigned through history. Its central research question is: what are the design principles of modern liberal citizenship, and how are they experienced in the contemporary US? Noting that modern liberal citizenship emerged from state security debates and that security concerns preoccupy those in the contemporary US, this article investigates not only how citizenship is designed but how safe citizenship is designed. As such, it is less concerned with the legal definition of citizenship than with the practical packaging of citizenship as part of a design for safe living.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".