Bibliographic record
Abstract
Since our October forecast events have conspired to worsen the outlook for the UK and global economy. Concerns about the solvency of banks across the globe have continued, and in some cases intensified. The inter-linkages of the global economy continue to be highlighted as the list of economies slipping into recession grows, even for those who have not suffered the direct shock of a crisis in their domestic banking system. Indeed what started as a problem in securities markets related to sub-prime lending in the US mortgage market has evolved into the near collapse of the global banking system. The UK has enjoyed the fruits of the rapid growth of financial intermediation over the past decade. However, such gains are being sharply reversed, as discussed on pp. 4–8 of this Review. The problem of access to credit for households and non-financial corporations still persists and, if anything, the situation seems to have deteriorated. As discussed on pp. 71–2 of this Review, Bank of England data suggest that lending by banks to households and businesses contracted in the final quarter of last year, even though £37 billion (2.6 per cent of money GDP) of new capital was injected into two major UK banking groups, effectively nationalising one of them (Royal Bank of Scotland).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.025 |
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".