Estimating the Accuracy of the Return on Investment (ROI) Performance Evaluations
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
Return on Investment (ROI) is one of the most popular performance measurement and evaluation metrics. ROI analysis (when applied correctly) is a powerful tool in comparing solutions and making informed decisions on the acquisitions of information systems. The purpose of this study is to provide a systematic research of the accuracy of the ROI evaluations in the context of information systems implementations. Measurements theory and error analysis, specifically propagation of uncertainties methods, were used to derive analytical expressions for ROI errors. Monte Carlo simulation methodology was used to design and deliver a quantitative experiment to model costs and returns estimating errors and calculate ROI accuracies. Spreadsheet simulation (Microsoft Excel spreadsheets enhanced with Visual Basic for Applications) was used to implement Monte Carlo simulations. The main contribution of the study is that this is the first systematic effort to evaluate ROI accuracy. Analytical expressions have been derived for estimating errors of the ROI evaluations. Results of the Monte Carlo simulation will help practitioners in making informed decisions based on explicitly stated factors influencing the ROI uncertainties.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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