Honesty in Accounting and Control: A Discussion of “The Effect of Information Systems on Honesty in Managerial Reporting: A Behavioral Perspective”*
Bibliographic record
Abstract
Introduction"Be honest to those who are honest, and be honest to those who are not honest."Lao Tzu "Where is there dignity unless there is honesty?"Cicero "Honesty: the best of the lost arts." Mark Twain "Honesty pays, but it don't seem to pay enough to suit some people."Frank McKinney "Kin" Hubbard Few areas create more controversy than those that question beliefs about one of the fundamentals of human nature, our propensity to tell the truth.As the first three quotations above illustrate, for thousands of years and across many different cultures, humankind has been drawn to the concept that "honesty is the best policy".However, as the last quotation implies, there is suspicion that few of us practice that policy when left to our own devices.In keeping with this latter view is agency theory, perhaps the dominant paradigm in management accounting research.Agency theory assumes that people act in their own self-interest, are work-averse and, hence, need to be controlled, monitored, and rewarded for doing what the "boss" wants them to do (Christensen and Feltham 2005).Although some agency theorists have proposed that individuals have some preference for honesty, they suggest that the honesty threshold is so low that most people would lie for a payoff
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".