Bibliographic record
Abstract
GDP growth in 2006 is estimated to have been 2.8 per cent per annum. This rate of growth is above our estimates of the trend rate of growth in the UK. On a quarterly basis the latest estimates suggest that growth above the trend rate occurred in the first half of 2006, with both quarters' growth rates at 0.8 per cent (see figure 1). The second half of the year saw quarterly growth rates at around the trend rate of 0.6 to 0.7 per cent. NIESR's estimate of monthly GDP implied that quarterly growth in the first quarter of this year was somewhat lower at 0.5 per cent because of a weak January. After this poor quarterly performance we expect an increase in economic growth due to household and government demand. Overall, this suggests a downward revision to our January forecast of 0.1 percentage points, although this is of little economic importance. We continue to expect the economy to moderate over the next three years. Our forecast is for GDP to grow by 2.7 per cent per annum this year, slowing to 2.6 and 2.4 per cent per annum in 2008 and 2009 respectively.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".