The Split-Screen Approach for Project Appraisal (Part I: The Theory)
Bibliographic record
Abstract
This paper illustrates an innovative approach to financial modeling of engineering decision-making and industrial projects. The approach is a minimal one, grounded as it is on three notions, two laws, and one matrix that combines them, called Split-Screen Matrix (SSM). This split-screen approach consists in linking the accounting and financial input data and systematizes them into the SSM, whose columns report the pro forma book values of capital (balance sheets), the corresponding income components (income statements), and the associated cash flows (cash-flow statements) while the rows show the project’s dynamical evolution. The SSMs are then linked via a continuous split-screen strip. To appraise the project, we use a pair of SSMs, namely, the project matrix and the benchmark Matrix (with the related strips), the latter containing the alternative amount invested and the associated foregone profit of a financial portfolio replicating the project’s cash flows. Using differences between the corresponding elements of the two strips, the economic profitability of the project can be easily measured, in both absolute terms (e.g., net present value, market value added, residual income) and relative terms (e.g., average return on assets, cash-flow return on capital). The accounting-and-finance engineering system (AFES) obtained with the split-screen approach is particularly helpful when using spreadsheet modeling because it does not require (knowledge and) use of financial spreadsheet functions. The application of this approach on spreadsheet modeling is essentially based on the continuous split-screen strip, here described, and is illustrated in a following paper (Baschieri and Magni 2023, “The Split-Screen Approach for Project Apraisal (Part II: Spreadsheet Modeling)”).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".