Bibliographic record
Abstract
The development of modern technologies in accounting models has got a great foundation for growth. Defining accounting as a system, it is obvious that the data of such a system will be based on quite familiar to us principles - mathematics. The earliest reference to the fact that the basis of accounting is just a mathematical component contained the work of an Italian monk L.Pacioli in Treatise on the accounts and records and till this day all the manual and computerized accounting systems in their logical construction are based on the principles and processes, that Pacioli described. However, if we try to trace the development of accounting from that time, we will see that the majority of the authors treated the mathematical component of accounting as granted. At the same time mathematical approach was often offset against the backdrop of the development of different accounting forms and techniques dictated by procedural accountants thinking. As far as any accounting operation can be represented by matrix equations this adds to the understanding of alternative processes of recording transactions and the mathematical generation of essential accounting reports
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.032 | 0.066 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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".