Exploring the Space of Model Comparisons
Bibliographic record
Abstract
Deploying machine-learning (ML) models is difficult and fraught with peril. Replacing an old model with a new one may introduce new biases and weaknesses that were easily over-looked. Unlike software updates where we have best practices (unit tests and the like), such best practices for ML are only now evolving. The ML deployment pipeline suffers further from a fracture: on the one side, one has the “data-science” (DS) pipeline, in which one extracts, loads, transforms, and maintains the vast lakes of data the models need to be trained on; on the other side, one has the “ML” pipeline in which experts test and evaluate models, often comparing many, for fitness for the task. To progress ultimately, these two pipelines must be integrated into a single DS/ML pipeline. We posit that doing so rests on model explainability and comparison.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.269 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".