Back to a Better Normal: Unemployment and Growth in the Wake of the Great Recession
Bibliographic record
Abstract
My life as a policymaker began the Monday before Thanksgiving in 2008 when President-Elect Obama announced his economic team. By the following Monday, we were all in Washington formulating the recovery policies. I vividly remember the Friday of that first week in December: the employment report for November was released showing that we had lost more than half a million jobs. It was clear that what might have been an ordinary recession a few months earlier was taking on ominous proportions. As I was briefing the President-Elect by phone, I found myself saying, “I am so sorry, the numbers are horrible. ” The President-Elect replied, “It’s not your fault—yet.” In the next few months, we saw even more terrible numbers. The American economy lost almost 3 million jobs between November 2008 and March 2009. Real GDP fell at an annual rate of 6.4 percent in the first quarter of 2009, and countries around the globe began to report staggering declines. The policy response was swift and bold. The Federal Reserve had taken dramatic actions when the crisis began, and continued to find creative ways to unfreeze credit markets. The TARP legislation, though deeply unpopular, provided crucial ammunition for dealing with the
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".