Bibliographic record
Abstract
Understanding variability in performance indicators 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. Like other writers, Wheeler suggests that variation though time can be separated into two elements. The first is common cause variation, sometimes known as noise or random variation. It has many different causes that include poorly defined operating procedures, measurement '... errors and wear ...' and tear in equipment. In the case of many public services, we must add the sheer variability in the cases with which staff must deal. Common cause variation can be reduced and should be reduced to a minimum. However, doing so can be expensive and may not be worth it if the cost is excessive. Special cause variation, often known as the signal, is usually caused by a change in the system that is being monitored. It indicates a real shift in performance and its detection is vital to the proper use of performance indicators.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".