Bibliographic record
Abstract
Abstract “National ID cards” are scare words in the United States, in England, and to a degree throughout the common-law world. If the instinctively negative reaction to ID cards were only an American phenomenon, one might dismiss it as yet another example of American exceptionalism—or, perhaps, another example of the U.S. failure to learn from foreign experience. But this powerful popular distaste for government-issued ID cards is not limited to the U.S. Similar and powerful reactions are found in England, Australia, and Canada. Indeed, in 2000, one could say that only four common-law countries had adopted ID cards in peacetime: Cyprus, Hong Kong, Malaysia, and Singapore. Meanwhile, however, ID cards are a routine and often uninteresting fact of life in the democracies of the civil-law world. That difference deserves exploration. (Some might argue that ID cards are inescapable, and that even the U.S. has them although it does not admit it, but this makes the difference in popular attitudes even more difficult to understand.) This chapter suggests that the U.S. hostility to ID cards is based on a romantic vision of free movement, and that the English view is tied to a related concept of “the rights of Englishmen.” I then suggest that these views distract from the real issues raised by contemporary national ID plans in the common and civil-law worlds. Today’s issues, I suggest, involve a complex set of data protection issues that have little to do with romantic stories of cowboys and motorists talking back to policemen, and a great deal to do with data storage and access.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".