U.S. productivity records smallest annual gain since 2011
Bibliographic record
Abstract
The productivity of American workers grew at a slower pace in fourth quarter and last year recorded the smallest annual gain in five years. The Labor Department says productivity grew at a 1.3 percent annual pace from October through December, down from 3.3 percent in the third quarter. Gains in worker productivity have slowed in the U.S. in recent years, and economists aren't sure why.Productivity measures output per hour worked....and increases are crucial for economic prosperity.The Labor Department says the productivity of American workers grew at a slower pace in the fourth quarter of last year....rising only 1.3 percent from October through December...which is down from 3.3 percent in the third quarter.For all of 2016, productivity rose only 0.2 percent...which is the smallest gain since a 0.1 percent gain in 2011.When workers are more productive, employers can afford to pay them more. And productivity gains, along with growth in the number of people working, determine how fast the economy grows.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.024 |
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; both teacher heads agree on what is shown here.
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".