Bibliographic record
Abstract
Our discussion in the last chapter focused on performance measures that relied solely on the information obtained from the confusion matrix. Consequently it did not take into consideration measures that either incorporate information in addition to that conveyed by the confusion matrix or account for classifiers that are not discrete. In this chapter, we extend our discussion to incorporate some of these measures. In particular, we focus on measures associated with scoring classifiers. A scoring classifier typically outputs a real-valued score on each instance. This real-valued score need not necessarily be the likelihood of the test instance over a class, although such probabilistic classifiers can be considered to be a special case of scoring classifiers. The scores output by the classifiers over the test instances can then be thresholded to obtain class memberships for instances (e.g., all examples with scores above the threshold are labeled as positive, whereas those with scores below it are labeled as negative). Graphical analysis methods and the associated performance measures have proven to be very effective tools in studying both the behavior and the performance of such scoring classifiers. Among these, the receiver operating characteristic (ROC) analysis has shown significant promise and hence has gained considerable popularity as a graphical measure of choice. We discuss ROC analysis in significant detail. We also discuss some alternative graphical measures that can be applied depending on the domain of application and assessment criterion of interest.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.027 |
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".