Bibliographic record
Abstract
If a modern Rip Van Winkle had fallen asleep two years ago and woken up now, he would wonder what had happened to the U.S. economy. Two years ago we were in the middle of an economic boom. Banks were eager to lend even at the cost of forgoing important covenants and corporate America (and the entire world) was producing at full steam, so much so that commodities prices were rising in anticipation of a future scarcity. Today we are quickly sliding into a deep recession. Banks are not lending and commodity prices are plummeting in expectation of a dramatic slowdown of production throughout the world. Neoclassical economic models cannot explain this dramatic change. There was no apparent shock to productivity nor a clear slowdown in innovation. Government has kept taxes low. The Federal Reserve has kept interest rates low and cut them even further. What happened? Everyone agrees that this crisis originated in the financial system. When Lehman Brothers defaulted and AIG had to be rescued by the government in September, the economy was still doing all right. The rate of growth during the second quarter was still a comfortable +2.8%. How could the default of an investment bank, with very limited lending to the real economy, have had such a disastrous effect?
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".