Merged Datasets: An Analytic Tool for Evidence-Based Management
Bibliographic record
Abstract
Many businesses fail to merge and analyze data effectively. When data are merged from diverse independent sources across a business — something that is now practical and inexpensive — it becomes possible to conduct rigorous pretest-posttest comparisons of complex datasets with a precision, speed, and breadth that have not been practical until now. This paper describes a straightforward method for merging independent datasets and using the compiled data to run informative quantitative analyses that facilitate sound decision-making. Our approach can help support several critical tasks in evidence-based management: documenting changes in the corporate culture; measuring linkages between “soft” perceptual variables and “hard” performance metrics; conducting rigorous pretest-posttest comparisons; and evaluating program effectiveness. We provide case-study examples using merged datasets, along with a brief discussion of experimental designs, underlying theory, pitfalls, impediments, and essential features.
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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 | 0.406 | 0.683 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.050 | 0.050 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".