U.S. Economic Outlook: Third Quarter 2019
Bibliographic record
Abstract
Highlights \n• In the third quarter of 2019, the U.S. economy grew at a 2.1% annualized rate. Growth was driven by consumer and government spending, and a buildup in inventories. \n• The third quarter of 2019 was the 41st consecutive quarter of growth and November the 125th month of consecutive growth for the U.S. economy. The current expansion is the longest on record. \n• The Federal Open Market Committee (FOMC) cut the federal funds rate three times this year, in July, September and October, due to slowing global growth and trade uncertainty, contributing to diminish recession fears. Federal Reserve Chairman Jerome Powell has announced a “wait-and-see” posture for future changes in monetary policy. \n• The nominal trade deficit narrowed by 7.6% in October to US$ 47.2 billion. It has narrowed in four of the past five months and is the narrowest since the first half of 2018. It is expected to make a positive contribution to growth in the fourth-quarter. \n• U.S. employers added 266,000 jobs in November, beating expectations and confirming the continued strength of the labor market. The unemployment rate dropped back to a historic low of 3.5%, and hourly earnings increased 3.1% over the past year, also exceeding estimates. \n• In November, U.S. consumer price inflation edged up (0.3%) following another rise in October (0.4%). Over the last 12 months, the all items Consumer Price Index (CPI) rose 2.1%. The core CPI was up 2.3%.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".