Foundational research in accounting: professional memoirs and beyond
Bibliographic record
Abstract
It was with particular pleasure that, several years ago, I accepted the invitation of ChuoUniversity to write a professional, biographical essay about my own experience with accounting. My relation with this university is a long-standing one. Shortly after two of my books, Accounting and Analytical Methods and Simulation of the Firm Through a Budget Computer Program, were published in the USA in 1964, Professor Kenji Aizaki (then at Chuo University) and his former student, Professor Fujio Harada, and later other scholars from Chuo University, began actively promoting my ideas in Japan. And after a two volume Japanese translation of the first of these books was published in 1972 and 1975 (through the mediation of Professor Shinzaburo Koshimura, then President of Yokohama National University), my research found fertile ground in Japan through continuing efforts of three generations of accounting academics from Chuo University. I suppose it is thanks to these endeavours that my efforts became so well known in Japan, and that during some three decades many Japanese accounting professors contacted me either personally or by correspondence. Then from 1988 to 1990 Prof. Yoshiaki Koguchi, again from Chuo University, came as a visiting scholar to the University of British Columbia, audited some of my classes, and became a good friend and collaborator, which further strengthened my ties to this university.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".