Public Expenditure in the UK: How Measures Matter
Bibliographic record
Abstract
Summary Studying spending over time requires reliable data. It is not clear that such data exist in the UK, however. The two published sources of functional spending numbers—the Office for National Statistics's ‘blue book’ and Her Majesty's Treasury's Public Expenditure Statistical Analyses (PESA)—rely on estimates of past spending, using a link year method, rather than recalculating actual spending figures when functional definitions change. We assess the various measures of spending in the UK. Specifically, we do two things. First, we present a new, third, set of spending numbers applying temporally consistent functional definitions to PESA microdata. Second, we compare the three measures. Our analyses indicate that the Office for National Statistics and PESA data differ quite markedly, especially for certain functions, i.e. in some cases the two measures imply completely different histories. The differences between the original PESA data and our new measures are less pronounced on average, though significant differences are evident, especially year by year.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".