Bibliographic record
Abstract
It is very unusual for a performance indicator to remain constant over any reasonable time period. When assessing performance, we need to know whether the differences seen from one period to the next are a sign of real change or are merely the result of variation that can be expected. Wheeler (1993) is a very readable book that suggests practical ways to understand and interpret variability in data. Wheeler argues that the output from any managed process will always display some variability, which means that performance through time must be interpreted very carefully. Wheeler provides several examples that clearly demonstrate the danger and difficulty in knowing whether apparent performance improvements are genuine or just random variation. This is an important question at all levels in the public sector, whether we are concerned with national economic performance or the micro performance of a single programme. For example, as this book is being written, economic commentators are sharing their views on the state of the UK economy. The UK’s Office of National Statistics has just published its estimate of growth in Gross Domestic Product (GDP) for the first quarter of 2010. The released figure, which may be later revised, is 1.1 per cent, which is larger than expected. Despite the excited comments of TV pundits and serious academics, no one seems to know whether this is a real improvement or just within the expected range of variation for this type of economic statistic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".