Bibliographic record
Abstract
Journalists and others have asked me whether the favourable RAE 2008 results for UK economics are believable. This is a fair question. It also opens up a broader and more important one: how can we design a bibliometric method to assess the quality (rather than merely quantity) of a nation’s science? To try to address this, I examine objective data on the world’s most influential economics articles. I find that the United Kingdom performed reasonably well over the 2001-2008 period. Of 450 genuinely world-leading journal articles, the UK produced 10% of them -- and was the source of the most-cited article in each of the Journal of Econometrics, the International Economic Review, the Journal of Public Economics, and the Rand Journal of Economics, and of the second most-cited article in the Journal of Health Economics. Interestingly, more than a quarter of these world-leading UK articles came from outside the best-known half-dozen departments. Thus the modern emphasis on ‘top’ departments and the idea that funding should be concentrated in a few places may be mistaken. Pluralism may help to foster iconoclastic ideas.
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 | 0.052 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.061 | 0.113 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".