Replication of Audit and Financial Accounting Research: We Do More than We Think
Bibliographic record
Abstract
SYNOPSIS There is a widespread concern that a “replication crisis” exists in the social sciences. Accounting researchers echo this claim and add that little accounting replication research is published. We carry out a conservative study to identify articles published in six leading accounting journals from 1970 to 2016 that attempt to replicate prior financial accounting and auditing research. We find 248 articles that attempted to replicate, in whole or in part, 298 published papers’ results typically in the context of extending the original finds. Highlights of our findings include: (1) the number and percentage of replicating articles have increased over the period; (2) 60 percent of all replication attempts are completely successful, 29 percent report mixed success, leaving 11 percent that fail to replicate. These findings suggest that the accounting academe publishes more replication research than previously documented and that published results are relatively robust when replicated. Data Availability: Data are available from the authors upon request.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.355 | 0.775 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".