Bibliographic record
Abstract
During the 2024 US presidential campaign Donald Trump declared that “the most beautiful word in the dictionary today is the word ‘tariff.’” His definition of a tariff is a tax that a government imposes on foreign imports and exports, although strictly speaking a tariff is a list of such taxes. Since his election he has been introducing such tariffs, for example a 25% charge on some imports from his neighbours Canada and Mexico and an extra 10% on Chinese imports, although the actual impositions vary from time to time, sometimes seemingly by whim. The other main meaning of “tariff” is, according to the Oxford English Dictionary (OED), “a classified list or scale of charges made in any private or public business.” This is the sense in which the word gains medical interest, since the reimbursements that pharmacists receive when they dispense medicines and other prescribable items in England and Wales are governed by a Drugs Tariff. As far as I am aware Trump does not intend to impose his kind of tariffs on prescribable medicines. We must hope that he never does.
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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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".