Barriers to Knowledge Creation in Management Accounting Research
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 In this article I address the questions posed to the 25th Anniversary of JMAR Panel on the management accounting topics we have established knowledge on, are currently working on, and where we might go in the future. In order to understand what we know, what we are currently learning about, and what we might learn in the future, I argue that we need to understand how knowledge in management accounting becomes legitimate. In the course of examining the two principal means of obtaining academic legitimacy I enumerate a number of barriers to the production of management accounting knowledge. These barriers include the relatively limited growth of management accounting research in the “top general interest” accounting journals, the lack of a globally acknowledged top niche journal in management accounting, and the perceptions of management accounting researchers about their craft as barriers to the production of such knowledge. Along the way I identify research topics that have waxed and waned over the 25 years since JMAR was first published. I conclude by suggesting a way forward that would require resolute leadership.
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.
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 | 0.041 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it