Bibliographic record
Abstract
Citizenship has been treated, since World War II, as a robust political and legal status. Recent political events have prompted the reassessment of the conditions under which it can be justly removed, however. Using the lens of democratic theory, I consider one particular instance of denationalization, namely, the withdrawal of citizenship from naturalized citizens when the granting state believes that the applicant ‘misrepresented’ themselves, that is, engaged in some form of deception, during the process of naturalization. There is an intuitive plausibility to the thought that if an applicant for citizenship lies or fails to provide all of the requested information, she should be denied citizenship. It seems equally plausible that, if citizenship status is nevertheless granted under these conditions, it can be permissibly removed. However, I argue that this conclusion is too quick: to be permissible, denaturalization procedures must be significantly constrained, in the ways that I outline.
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 | 0.050 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".