New Polystochastic Statistical Inference in Social Sciences - Defining new Rules and Thresholds
Bibliographic record
Abstract
The Null Hypothesis Significance Testing (NHST) framework has sparked considerable debate within the scientific community, leading to numerous studies advocating for a re-evaluation of the current system. New polystochastic statistical inference defines methods of statistical inference that integrate rules and thresholds for both rejecting the null hypothesis and confirming the alternative hypothesis. This approach unifies the control of respondents' influence on statistical significance and introduces criteria such as effect size and Bayesian inference for confirming the alternative hypothesis. Unlike NHST, polystochastic statistical inference controls Type I error (p-value) and aims to optimize the confirmation of evidence without increasing the risk of Type II errors.
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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 | 0.169 | 0.370 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".