The civics test: a political or educational tool for creating the perfect citizen? A historical overview of forms and processes of naturalization in the United States
Bibliographic record
Abstract
Naturalization, or the process through which citizenship is granted to a foreigner, is a process that has begun to increasingly look like that of the school. In the United States, as in many other countries, one of the main features of the naturalization is the civics test. This paper aims to document the historical development of naturalization procedures in the United States and shed light on how schoolish tools were introduced to decide who can be offered or denied American citizenship. Much of past research has critiqued the civics test for its unreliability, or difficulty for even natives. We argue, however, that the current civics test is rather a product of a system that began without a solid foundation. In an attempt to avoid fraud and control efficiency, the United States Citizenship and Immigration Services (USCIS) has promoted the use of a test that devalues the importance of the choice to re-align loyalties to a country and regulates it to memory testing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".