Identifying the Big Shots—A Quantile-Matching Way in the Big Data Context
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
The prevalence of big data has raised significant epistemological concerns in information systems research. This study addresses two of them—the deflated p -value problem and the role of explanation and prediction. To address the deflated p -value problem, we propose a multivariate effect size method that uses the log-likelihood ratio test. This method measures the joint effect of all variables used to operationalize one factor, thus overcoming the drawback of the traditional effect size method (θ), which can only be applied at the single variable level. However, because factors can be operationalized as different numbers of variables, direct comparison of multivariate effect size is not possible. A quantile-matching method is proposed to address this issue. This method provides consistent comparison results with the classic quantile method. But it is more flexible and can be applied to scenarios where the quantile method fails. Furthermore, an absolute multivariate effect size statistic is developed to facilitate concluding without comparison. We have tested our method using three different datasets and have found that it can effectively differentiate factors with various effect sizes. We have also compared it with prediction analysis and found consistent results: explanatorily influential factors are usually also predictively influential in a large sample scenario.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it