Finding Multidimensional Simpson's Paradox
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Finding and analyzing Simpson's paradox, a well known statistical phenomenon, has found many applications. While the existing literature focuses on only analyzing the causes of identi ed Simpson's paradox, there is no systematic analysis on Simpson's paradox in multidimensional spaces. In this paper, we develop a simple yet practical approach to automatically identify all Simpson's paradox instances formed by various sub-populations and separator attributes in a multidimensional data set. Moreover, we analyze the distribution of the multidimensional Simpson's paradox instances on three real data sets with respect to dimensionality, size of sub-populations, participation of individual records, redundancy, and more. We obtain a series of interesting observations about a few questions that have never been asked before. The results open doors to a few interesting directions for future study. Moreover, this paper is an outcome from a high-school student summer research internship. It re ects our on-going e ort in promoting data science research to youth and high school students.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it