Pitfalls in studying “big data” from operational scenarios
Bibliographic record
Abstract
Analyzing a larger dataset is sometimes assumed, in itself, to confer a greater degree of validity to the results of a study. In biometrics, analyzing an “operational” dataset is also sometimes assumed, in itself to confer a greater degree of validity. And so studying a large, operational biometric dataset may seem to guarantee valid results. However, a number of basic questions should be asked of any “found” big data, in order to avoid pitfalls of the data not being suitable for the desired analysis. We explore such issues using a large operational iris recognition dataset from the Canada Border Services Agency's NEXUS program, similar to the dataset analyzed in the NISTIREX VI report.
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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 | 0.217 | 0.522 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".