Some Clarification to the Evolution of the Electronic Spreadsheet
Bibliographic record
Abstract
ABSTRACT As early as 1961 Mattessich suggested (in an article in The Accounting Review) to use budget simulation in form of a computerized spreadsheet. This was followed up by him in a mathematical model, outlined in his book Accounting and Analytical Methods (Mattessich 1964a) with a corresponding computer program (in FORTRAN IV on mainframe computers), including illustrations in a companion volume (Simulation of the Firm through a Budget Computer Program, Mattessich 1964b). Five years later (in 1969) Rene Pardo and Remy Landau co-presented “LANPAR” (LANguage for Programming Arrays at Random) at Random Corporation. This electronic spreadsheet type was also used on mainframe computers for budgeting at Bell Canada, AT&T, Bell operating companies, and General Motors. In 1978, Dan Bricklin and Robert Frankston introduced VisiCalc, the first commercialized spreadsheet program for personal desktop (Apple) computers. This program became the trailblazer for future developments of electronic spreadsheets.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".