Points to Consider When Self‐Assessing Your Empirical Accounting Research
Bibliographic record
Abstract
Abstract We provide a list of points to consider (PTCs) to help researchers self‐assess whether they have addressed certain common issues that arise frequently in accounting research seminars and in reviewers’ and editors’ comments on papers submitted to journals. Anticipating and addressing such issues can help accounting researchers, especially doctoral students and junior faculty members, convert an initial empirical accounting research idea into a thoughtful and carefully designed study. Doing this also allows outside readers to provide more beneficial feedback rather than commenting on the common issues that could have been dealt with in advance. The list, provided in the appendix, consists of five sections: Research Question; Theory; Contribution; Research Design and Analysis; and Interpretation of Results and Conclusions. In each section, we include critical items that readers, journal referees, and seminar participants are likely to raise and offer suggestions for how to address them. The text elaborates on some of the more challenging items, such as how to increase a study's contribution, and provides examples of how such issues have been effectively addressed in previous accounting studies.
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 | 0.094 | 0.491 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".