When Do Analysts Adjust for Biases in Management Guidance? Effects of Guidance Track Record and Analysts' Incentives
Bibliographic record
Abstract
The above article has been retracted at the request of authors Robert Libby and Hun‐Tong Tan, in agreement with the Editor‐in‐Chief, Patricia C. O'Brien, the copyright holder, the Canadian Academic Accounting Association (CAAA), and Wiley Periodicals, Inc. Bentley University conducted an investigation confirming that Dr. J. E. Hunton, while a faculty member at Bentley University and without the knowledge of his co‐authors, engaged in research misconduct, specifically data fabrication. Dr. Hunton provided the data used in the above study, and did not respond to a request for comment. Based on Bentley's initial report and further investigation into details specific to this paper, we conclude that the validity of the data cannot be confirmed. To correct the academic literature and maintain standards of academic integrity, we therefore retract the paper. The article was published online in Contemporary Accounting Research on 13 May 2010, in Wiley Online ( wileyonlinelibrary.com ). Reference Tan , H.‐T. , R. Libby , and J. E. Hunton . 2010 . . Contemporary Accounting Research 27 (): 187 – 208 . doi: 10.1111/j.1911‐3846.2010.01006.x
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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 | 0.141 | 0.699 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".