The Split-Screen Approach for Project Appraisal (Part II: Spreadsheet Modeling)
Bibliographic record
Abstract
This paper employs the newly conceived accounting-and-finance engineering system (AFES) described in a previous paper (Magni 2023, “The Split-Screen Approach for Project Appraisal (Part I: The Theory)”), addressed to the analysis of capital asset investments. In this second part, we show how to implement this theoretical framework onto a spreadsheet software. We guide the analyst step by step, cell by cell, to the creation of the Split-Screen Matrices describing the project film. Because the AFES is based on two arithmetic relations (law of motion and law of conservation), we can use a minimal approach to modeling, with a frugal use of the most common spreadsheet functions (essentially INDEX and MATCH) and no use of the traditional financial functions, yet fulfilling the requisite of clearness, transparency, consistency, and ease of use. Starting from the informal description of the project, we build the model by breaking it down to 7 modules. The spreadsheet model is available online (see link provided in the paper).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".