Andrew G Haldane: Rethinking the financial network Speech by Mr Andrew G Haldane, Executive Director, Financial Stability, Bank of England, at
Bibliographic record
Abstract
was recorded in Guangdong Province, China. Panic ensued. Uncertainty about its causes and contagious consequences brought many neighbouring economies across Asia to a standstill. Hotel occupancy rates in Hong Kong fell from over 80 % to less than 15%, while among Beijing’s 5-star hotels occupancy rates fell below 2%. Media and modern communications fed this frenzy and transmitted it across borders. In North America, parents kept their children from school in Toronto, longshoreman refused to unload a ship in Tacoma due to concerns about its crew and there was a boycott of large numbers of Chinese restaurants across the United States. Dr David Baltimore, Nobel prize winner in medicine, commented: “People clearly have reacted to it with a level of fear that is incommensurate with the size of the problem”. The macroeconomic impact of the SARS outbreak will never be known with any certainty. But it is estimated to stand at anything up to $100 billion in 2003 prices. Across Asia, growth rates were reduced by SARS by between 1 and 4 percentage points. Yet in the final reckoning, morbidity and mortality rates were, by epidemiological standards, modest. Only around 8000 people were infected and fewer than 1000 died.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".