Bibliographic record
Abstract
In this appendix, we outline some aspects of how the results of an experiment are evaluated. Although this clearly cannot be a comprehensive treatment of data analysis techniques, even as it applies to the results presented in this book, it at least may acquaint the reader with some of the relevant concepts. For readers with some background in statistical methods, the appendix also documents some procedures for calculating the likelihood ratios we use to compare models. Experimental Design In the vocabulary of experimental design, a manipulated independent variable is referred to as a factor , and each possible value of that variable is a factor level . For example, if one presents readers with two different versions of a story, one would say that the factor of story version has two levels. Often experiments have more than one factor. In a factorial experiment , each possible level of one factor is combined with each possible level of the other factors, and each combination of factor levels determines a particular experimental condition. For example, suppose the factor of story version was factorially combined with the factor of reading goal with two levels: reading to identify the narrator's point or reading to identify the plot events. In such a design, there would be four conditions: version 1 read for narratorial point; version 1 read for plot events; version 2 read for narratorial point; and version 2 read for plot events. Two types of results can be examined in a factorial experiment.
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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 | 0.007 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.591 | 0.207 |
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".