Learning from Data, and Tools for the Task
Bibliographic record
Abstract
We begin by illustrating the interplay between questions of scientific interest and the use of data in seeking answers. Graphs provide a window through which meaning can often be extracted from data. Numeric summary statistics and probability distributions provide a form of quantitative scaffolding for models of random as well as nonrandom variation. Simple regression models foreshadow the issues that arise in the more complex models considered later in the book. Frequentist and Bayesian approaches to statistical inference are contrasted, the latter primarily using the Bayes Factor to complement the limited perspective that p-values offer. Akaike Information Criterion (AIC) and related "information" statistics provide a further perspective. Resampling methods, where the one available dataset is used to provide an empirical substitute for a theoretical distribution, are introduced. Remaining topics are of a more general nature. RStudio is one of several tools that can help in organizing and managing work. The checks provided by independent replication at another time and place are an indispensable complement to statistical analysis. Questions of data quality, of relevance to the questions asked, of the processes that generated the data, and of generalization, remain just as important for machine learning and other new analysis approaches as for more classical methods.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".