Bibliographic record
Abstract
The Office for National Statistics' (ONS) Preliminary Estimate of GDP suggests the quarterly rate of growth accelerated rapidly this year, from 0.3 per cent in the first quarter to 1.1 per cent in the second quarter (figure 1). This positive news should not be taken as a strong indicator of the prospects for the next year, as a quarter of strong growth is often matched by a subsequent weak quarter. In the first quarter of this year demand was driven by government spending and the continued moderation in the rate of inventory reduction, contributing 0.5 and 0.3 percentage point, respectively to GDP growth. Data for the second quarter of this year suggest that the expansion was spread relatively evenly, with a particularly robust contribution from the construction sector. Since the estimates are derived on an output measure we have little information by way of the sources of demand. We expect that much of the demand came from the same sources as in the first quarter. It is possible that some output was delayed from the first to the second quarter due to the bad weather, while retail sales were perhaps boosted by expenditure on electrical goods related to the World Cup in South Africa. We forecast a sharp moderation in the rate of growth in the second half of this year, as figure 1 shows.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.050 |
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".