Fifteen Thoughts on Brexit for the Cosmopolitan Intellectual Elite
Bibliographic record
Abstract
1. When I pass through Frankfurt or Amsterdam (and almost inevitably, it is one or the other) from a visit to North America or Asia, I use my British passport to skip the long lines for non-Europeans. Likewise when I vacation around Europe. Embarrassingly, these airport conveniences, provided through EU-wide agreements, are pretty much the only way that I’ve directly experienced life as a British subject. My wife, by contrast—like me a Canadian who is also a British citizen by happenstance rather than by upbringing—is able to live and work in Italy without visa or hassle, pay Italian taxes, and receive Italian health care. To this extent, Britain’s membership in the EU has worked incredibly well for the two of us. It is a source of just ever-so-much convenience. But that is no measure of its justice. To put it more sharply, and less selfishly: by me, 70 years without war between European powers is a victory of the European project. But I don’t have to e.g. post notices about the allergens in the food I’m serving, compete for low-paid service work, or live on a pension reduced by fiat from Brussels. The post-war project of economic integration through law may have been sufficient to underwrite the peace, but that does not make the EU necessary.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.065 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".