Bibliographic record
Abstract
ABM simulations.See Agent-based modeling (ABM) simulations Accountants, 4, 139 Australian, 147 Canadian, 148 career anchors, 158 forensic, 27 millennials, 31, 149, 150, 158 older generations, 31 organizational citizenship, 8 personality, 121 professional, 142, 152, 157 public accounting industry, 23 risk averse, 4 social desirability response bias (SDRB), 9 survey, 7 Taiwan, 7 (un)ethical behavior, 9 Accounting choice disclosure, 59, 67 agency risks, 53 anchor-and-adjustment phenomenon, 57, 58 conservative accounting choice, 53, 56-57 disconfirming attributes, 58 execution risk, 53 market risk, 54 overpayment risk, 53, 55 revenue outcomes, 53, 54 Account risk disclosure, 59, 67 agency risk, 55 anchor-and-adjustment phenomenon, 57, 58 conservative accounting choice, 56-57 disconfirming attributes, 58 economic realities, 55 risk-averse behaviors, 55 Accredited investors, 50 ACFE.See Association of Certified Fraud Examiners (ACFE) Agency risks, 53, 55 Agent-based modeling (ABM) simulations, 27 Aggressive accounting choice, 48, 49, 55, 63 Agreeableness, 120-121, 123, 125, 128, 131-132 Alibaba, 23 Altruism, 7, 8 Analysis of variance (ANOVA), 12, 13, 64-65, 67, 155 Anchor-and-adjustment phenomenon, 57, 58 Angel investors accounting choice disclosure.See also Accounting choice disclosure, 48 account risk disclosure, 55-60 angel valuation judgments, 51-53 "changes" approach, 61 demographic information, 62, 63 disconfirming disclosure, 49, 68 experimental design, 61 FASB revenue recognition standard, 60 hypotheses testing, 64-66 limitations, 69-70 manipulation checks, 63 non financial vs financial models, 63-64 private company investment, 48 prospect theory, 49 revenue account, 61-62 seed equity investment (SEI) contexts, 49-50 straight-equity funding, 60
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.780 | 0.816 |
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".