Deception in Management Accounting Experimental Research: “A Tricky Issue” Revisited
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Management accounting (MA) scholars generally accept that our subject matter requires a multidisciplinary approach. Broadly speaking, there are two main views from different base disciplines about experimental deception: “deception if necessary” (social psychology) and “deception should be banned” (experimental economics). We aim to develop a common understanding within the MA research community about what constitutes deceptive research practice. We review arguments supporting the two main views and analyze the transfer of their norms into MA research. We develop a framework that evaluates the need for and potential consequences of using deception. Our analysis implies careful consideration of the decision to employ deception and case-by-case editorial review of experiments employing deception are necessary. In the long run, the MA research community may consider if an explicit policy on the role of deception in MA research is warranted or whether a case-by-case approach, as advocated by us as an interim measure, is sufficient.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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 it