Bibliographic record
Abstract
The prominence of the “replication crisis” in social sciences has spurred concern regarding the reliability of published research. Accounting researchers share such apprehensions, acknowledging the perceived scarcity of published accounting replication studies. To investigate this belief, we analyze annual replication rates and the number of replicated original articles from three prominent accounting journals: Contemporary Accounting Research (CAR), Journal of Accounting and Economics (JAE), and Review of Accounting Studies (RAST). Two sample periods, spanning from 1970-2015 and 2016-2021, reveal a more than doubled increase in both 1) the average annual replication rate, rising from 3.13 to 8.67 times, respectively and 2) the average number of replicated original articles per year, advancing from 5.33 to 12.77 original articles, respectively. These findings challenge conventional beliefs surrounding the replication crisis in accounting, revealing a growing volume of published accounting replication research and replicated original articles. The significant increase observed, even within this small sample of three journals, suggests that accounting replication is more prevalent than thought. As ongoing analysis extends to a broad selection of prominent and smaller journals, the distinguished pattern emerging within the current sample suggests a potential continuation of this trend across accounting research.
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 | 0.325 | 0.759 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".