Accounting Students' Sensitivity to Attributes of Information Integrity
Bibliographic record
Abstract
ABSTRACT This study investigates how students incorporate information integrity impairments into judgments and judgment confidence. The effects of four information integrity attributes (completeness, currency, accuracy, and authorization) were examined. Our results show that accounting students incorporate some information integrity attributes into their judgments and judgment confidence. As the severity of the integrity impairments increased, accounting students assigned more weight to information integrity impairments in judging the performance of division managers. We find that accounting students' judgments are incorrectly influenced by information integrity. Performance judgments were positively correlated with the level of information integrity as if the accounting students were rewarding or penalizing managers for the integrity of the information. Our results indicate that as information integrity impairments increase, students are more interested in postponing their judgments to seek additional information. Given the importance of information integrity in the accounting profession, it is critical that accounting students develop the ability to appropriately consider information integrity impairments when making judgments. The results of this study are important to accounting instructors that teach information integrity issues in their courses.
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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 | 0.005 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".