U.S. Economic Outlook: 2019 in review and early 2020 developments
Bibliographic record
Abstract
Highlights \n \n• The record long U.S. economic expansion is coming to an end, as a result of the COVID-19 pandemic. Shutdowns to stem the spread of the virus have already had an impact on the economy, and are already visible in some preliminary data for March, ranging from jobless claims to factory output. The U.S. government will release its initial estimate for the first quarter of 2020 at the end of April, and a decline in growth is expected, followed by an even worse decline in the second quarter. \n \n• Three stimulus packages were approved by the U.S. Congress in March, aiming to address the impact of the COVID-19 pandemic on households and businesses. The Federal Reserve has cut interest rates to the zero-lower bound, offered unlimited quantitative easing, and deployed old tools (used in the 2008 financial crisis) and new, aimed at keeping financial markets functioning. \n \n• A U.S. recession in the first half of the year is now the baseline forecast according to market projections. The outlook remains highly uncertain and constantly changing, as new estimates of the impact of the pandemic are made and government actions further restricts the population’s mobility and economic activity.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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".