Bibliographic record
Abstract
Thank you very much for this opportunity to speak to the Booth School San Francisco alumni club and the CFA Society. To those of you who attended the University of Chicago, I want to note two proofs of your brilliance. First, you went to one of the very best schools in the United States for studying business and economics. And, second, in light of the images we’ve seen of snowbound cars on Lake Shore Drive, you had the good judgment to move to my home state of California, where we trade off the occasional earthquake for balmy winters. This evening I’m going to offer my thoughts about the economy and talk about Federal Reserve policy during this period of recovery. I will also address several topics that have made it particularly challenging for monetary policy at this juncture. These include the question of whether the current very high level of unemployment is primarily cyclical or structural. That is, does the current 9 percent unemployment rate mostly reflect weak demand in the economy or, alternatively, strong labor-market frictions that make it harder than usual to match jobs and workers. The answer to that question is vital in determining the noninflationary level of unemployment. I should stress that these remarks represent my own views and not necessarily those of my Federal Reserve colleagues.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.282 | 0.243 |
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".