Ramifications of Covid-19 on management accounting teaching and research
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how COVID-19 has affected the author’s management accounting teaching and research. Design/methodology/approach This paper is a reflection essay on management accounting. Findings The author realized that the textbooks, because of the lack of integration among management accounting techniques, do not prepare the students with the ability to make the quick changes required by COVID-19. The author expects that they will have to introduce integration to the management accounting textbooks and courses. Qualitative research will be helpful in identifying the management accounting techniques now integrated in practice. The author further expects the beneficial practices that were learned from online and remote teaching during the pandemic will be with them into the future. Research limitations/implications This paper is limited as it is a personal reflection. Practical implications COVID-19 has required organizations be increasingly agile, particularly in the use of budgets and other management accounting techniques. Social implications Opportunities are identified for improving the teaching and use of management accounting, especially regarding strategy and budgeting. Originality/value The extreme nature of pandemics intensifies the observations of the functioning of disciplines such as management accounting. Everyone learns from extreme experiences.
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 | 0.125 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".