Bibliographic record
Abstract
Inferences are never assumption free. Data summaries that do not account for all relevant effects readily mislead. Distributions for the Pearson correlation and for counts, and extensions accounting for handling extra-binomial and extra-Poisson variation are noted. Notions of statistical power are introduced. Resampling methods, the bootstrap, and permutation tests, extend available inferential approaches. Regression with a single explanatory variable is used as a context in which to introduce residual plots, outliers, influence, robust regression, and standard errors of predicted values. There are two regression lines – that of y on x and that of x on y . Power transformations, with the logarithmic transformation as a special case, are often effective in giving a linear relationship. The training/test approach, and the closely allied of cross-validation approach, can be important for avoiding over-fitting. Other topics include one- and two-way comparisons, adjustments when there are multiple comparisons, and the estimation of false discovery rates when there is severe multiplicity. Discussions of theories of inference, including likelihood, and Bayes Factor and other Bayesian perspectives, ends the chapter.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".