The future of macroeconomics : a discussion of a paper by John Muellbauer
Bibliographic record
Abstract
Let me start out by saying that, in a 10 - minute discussion it's difficult to do justice to everything that's in John's very interesting paper. I'm going to draw on what I take to be three themes. The first theme, that John did not say a huge amount about , is that clearly some of the DSGE models we've been working with have not been particularly successful. \n \nSecondly, John mentioned a couple of people that he's found very insightful. One is David Hendry and one is Joe Stiglitz and I echo that sentiment. I n my first job at the University of Toronto I went to see Joe Stiglitz give a talk. At the time, I didn't really have a clear thesis topic. My thesis ended up being inspired by that talk; so the notion that there are some very important insights in what we call the information revolution in economics is one that I endorse wholeheartedly. \n \nFinally, one of the things I'd like to talk about in this discussion is what we can learn from the information revolution. My view is that what we can learn is perhaps even a little more radical than some of the things that John drew attention to. \n
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".