BANK OF ENGLAND MONETARY POLICY – FROM STABILITY TO FINANCIAL CRISIS AND BACK?
Bibliographic record
Abstract
I would like to thank Matthew Corder and Jake Horwood for research assistance and I am also grateful for helpful comments from other colleagues. The views expressed are my own and do not necessarily reflect those of the Bank of England or other members of the Monetary Policy Committee. It is a particular pleasure for me to have the opportunity to return to NIESR, where I worked in the early 1980s in the aftermath of a previous recession, to talk in my present role as a policy-maker. At that time, one of my responsibilities was to monitor and project world trade- I am very thankful that I didn’t have to deal with the analysis of a fall in trade of the magnitude seen in this recession, when world trade 1 fell by a cumulative 18 % over the fourth quarter of 2008 and the first quarter of 2009, before starting to recover – up 11 % on the low point by the end of 2009. Today I want to pursue a number of monetary policy issues, looking back over the almost nine years I have been on the MPC and seeking to draw lessons from that experience for the very difficult decisions that seem likely to face the Committee over the next couple of years. In particular, I will make some observations about the problems that policymakers inevitably face in assessing the implications for inflation of the pressure of demand on the economy’s supply capacity, and also about the time
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".