Bibliographic record
Abstract
In the era of AI and Data Science, the extensive use of big databases for various purposes, including crime investigations, medical studies, and general population profiling, leads increasingly to the possibility of random database matches driven merely by coincidence akin to the famous birthday paradox. As databases swell in size and complexity, we show in this paper that under some circumstances the likelihood of coincidental matches between seemingly unrelated entries increases dramatically. These extraneous matches can inadvertently mislead investigators and analysts, ultimately resulting in incorrect source attributions.Applying the mathematics of generalized birthday problems, this paper uses an expository approach to delve into the intricacies of data dredging across diverse data sets, emphasizing the need for caution when interpreting results obtained through post-hoc analysis. We explore the potential consequences of relying on post-facto data-driven storytelling, highlighting the dangers of attributing meaning to even matches that occur with seemingly extraordinary odds.
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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 | 0.048 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".