Bibliographic record
Abstract
PARTICIPANTS I Beacon Economics = Los Angeles, California; Conf. Board = Conference Board, New York, New York; Fannie Mae = Fannie Mae, Washington, D.C.; IHS = IHS Global Insight, Eddystone, Pennsylvania; GSU-EFC = Georgia State University, Economic Forecasting Center, Atlanta, Georgia; Moody's Economy = Moody's Economy.com, Westchester, Pennsylvania; Mortgage = Mortgage Bankers Association, Washington, D.C.; NAM = National Association of Manufacturers, Washington, D.C.; Northern Tr = Northern Trust Company, Chicago, Illinois; Perryman Gp = Perryman Group, Waco, Texas; Royal Bank of Canada, Toronto, Ontario, Canada; SP UBS = UBS Bank, Salt Lake City, Utah; US Bank = U.S. Bank, Minneapolis, Minnesota; US Chamber = U.S. Chamber of Commerce, Washington, D.C.; Wells Fargo = Wells Fargo Bank, San Francisco, California.Consensus expects the country's GDP growth rate to remain in the neighborhood of 2.3% well into the second quarter of 2016 even though real GDP declined (now at a negative 0.2%) in the first quarter of 2015. Specifically, Rajeev of the Economic Forecasting Center at Georgia State University's J. Mack Robinson College of Business expects the U.S. economy to bounce back in second quarter because of WOW. The three components of WOW shaved off close to 2.5% of U.S. growth in the first quarter, Dhawan said. (WOW stands for weather, oil, and the world economy.) GDP report showed clear damage from these three factors.Dhawan mentioned further in his report that unusually cold weather in the Northeast during the first quarter resulted in a reduction of nondurable consumption goods (to a negative 0.3%), spending on utilities (heating) increased, and overall gasoline savings were wiped away. We've almost reached the bottom, with oil rig counts having dropped sharply with only a little bit to go, wrote Dhawan. But prices will not reach the heights of $120 a barrel anytime soon. I expect oil to start creeping up to $70/barrel by year's end and stay in that range for the coming year, he added. Finally, the world economy factor influenced the real GDP due to the dragging recovery of China (now at 7%, down from double digits) and the European Central Bank's bond-buying program. Chinese economy's slow-paced recovery affects the emerging markets because of supply chain connections. Eurozone's challenge is related to a potential Greek rescue operation and the trillion-dollar liquidity injection (bond buying program) by the European Central Bank, which results in negative government bond yields. Consequently, these factors led to a decline in exports (now at a 2.3% decline).CONSUMERSThe improvements in employmentand increases in consumers' personal disposable income positively affect consumption and, subsequently, growth in the economy. However, as pointed out, the weather factor, although a temporary one, played significant impact along with the changes in crude oil prices (now, at US$59/barrel). …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".