Bibliographic record
Abstract
We know very well by now that the institutions managing the epidemiological research environment-agencies, schools and journals-do not sufficiently incentivize getting the best answers to causal questions. 1,2 Sadly, they do not even incentivize asking well-formulated causal questions in the first place. The dominant metrics of success are publication counts, impact factors and external funds received, none of which necessarily reflect the priority of sober and honest accounting of study limitations and biases. If you are a NASA engineer and a spaceship crashes because you messed up, heads are going to roll. But if you are an epidemiologist who told people to eat margarine when they were better off eating butter, you never have to give any money back to the funders. For outright fraud, maybe. Yet for simple incompetence, or even stubborn and wilful incompetence, there are generally no retractions, no penalties, no demotions, no apologies. Thus we perversely incentivize 'spin', obscuring true weaknesses and limitations, selecting the most 'exciting' results to highlight (often gauging excitement by the smallness of the P-value), and anything else short of outright fabrication that gets the submission past the reviewers and into a 'top' journal. The review process itself is played like a football game, dodging and weaving through the opposing team's defences to score a goal. This does not look like a scientific community dedicated to deducing the best answers to the best questions. Rather, this is a sadly cynical portrayal of our field, and one consistent with the paper published in this issue by Blum et al. As they explain, the E-value is a sensitivity analysis for uncontrolled confounding that is dumbed down to a single number. 7 Under some rather bizarre conditions, it represents the minimum strength of an unmeasured confounder that could nullify the reported finding. The inventors of the E-value have been forthright about the rather unrealistic set-up required to boil three parameters of an unmeasured confounder (association with exposure, association with outcome and target-population-specific
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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 | 0.030 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".