Bibliographic record
Abstract
We launch the IJE’s Data Resource Profile (DRP) series with the famous words attributed to the statistician and management scientist, W Edwards Deming. Deming was lauded by prime ministers and presidents for his transformational role, founded on carefully designed data collection and appropriate analysis, in improving quality processes in Japanese industry after World War Two. Deming was trained in engineering, mathematics and mathematical physics. In 1936 he studied statistical theory with RA Fisher and Jerzy Neyman in London. He and Neyman later convinced the US Census Bureau to adopt Neyman’s groundbreaking work on probability sampling1 for the 1940 US Census. Deming went on to be a global figure in statistics and in management science.2 What may be of interest to epidemiologists is that within the core of Deming’s work was the goal of understanding ‘common causes’ and ‘special causes’ of variation. In Deming’s case, it was variation in quality of some product, such as cars; what caused some manufacturers to make many cars that were ‘lemons’? Special cause variation in products was inherently unpredictable, even probabilistically. Davey Smith has recently explicated the potential ‘Gloomy Prospect’ for epidemiological understanding of this type of cause in health sciences.3 Deming was clear that it was vital to not confuse special (individual failure) and common (system failure) causes. Deming’s ideas resonate with Geoffrey Rose’s thinking on the causes of sick individuals (special causes) and sick populations (common causes).4 Rose pointed out that if everyone in a population smoked the same number of cigarettes, then lung cancer would be found to be associated with genetic susceptibility. In the face of a ubiquitous common cause (smoking), a special cause (genes) would be associated with variation in lung cancer risk. This sort of thinking, and perhaps confusion, is reflected in current debates about the role of environmental and genetic causes of obesity.5,6
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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 | 0.089 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.029 | 0.046 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.013 | 0.047 |
| Insufficient payload (model declined to judge) | 0.069 | 0.083 |
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".