Bibliographic record
Abstract
We conclude the discussion on various aspects of performance evaluation of learning algorithms by unifying these seemingly disparate parts and putting them in perspective. The raison d'être of the following discussion is to appreciate the breadth and depth of the overall evaluation process, emphasizing the fact that such evaluation experiments should not be put together in an ad hoc manner, as they are currently done in many cases, by merely selecting a random subset of some or all of the components discussed in various chapters so far. Indeed, a careful consideration is required of both the underlying evaluation requirements and, in this context, of the correlation between the different choices for each component of the evaluation framework. This chapter attempts to give a brief snapshot of the various components of the evaluation framework and highlights some of their major dependencies. Moreover, for each component we also give a template of the various steps necessary to make appropriate choices along with some of the main concerns and interrelations to take into account with respect to both, other steps in a given component and other evaluation components themselves. Unfortunately, because of the intricate dependencies between various steps as well as components, it might seem necessary to make simultaneous choices and check their compatibility. The general model evaluation framework should serve as a representative template and not as a definitive guide.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.156 | 0.081 |
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".