Cryptocurrencies in accounting schools?
Bibliographic record
Abstract
Purpose This study aims to investigate the extent to which newly certified public accountants (CPAs) and accounting graduate students possess a comprehensive understanding of cryptocurrencies and the skills they have acquired throughout their education. Design/methodology/approach A qualitative analysis was used through semi-structured interviews to obtain an in-depth insight into cryptocurrencies, which could not be investigated easily through quantitative methods, and to provide an understanding of the context for cryptocurrencies from CPA and non-CPA students' points of view. This was in addition to focusing on understanding the differences between the students' thoughts. Findings This study found that recent accounting graduates and CPA members have the least awareness of cryptocurrencies, likely due to a lack of professors' comprehension or exposure to the concept. However, students involved in forensic courses provided more information about cryptocurrencies compared with other students. Research limitations/implications The data are limited to only a single country. Given that cryptocurrencies are a relatively new notion in accounting, there is an alarming lack of legislation. Further, the authors found that recent accounting graduates and CPAs had the same level of knowledge of cryptocurrencies, most probably due to a lack of exposure during their education and academics' limited understanding of the concept. Practical implications The students' differing answers about cryptocurrencies show differences in their current level of understanding of cryptocurrencies. Originality/value This study has identified that the vast majority of accounting graduates lack adequate knowledge about cryptocurrencies or access to adequate resources, despite understanding the fundamental concepts of cryptocurrency.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".