On theoretical engorgement and the myth of fair value accounting in China: a reply
Bibliographic record
Abstract
Purpose This article is a reply to “On theoretical engorgement and the myth of fair value accounting in China” Nobes (2019) from the authors of “Adaptability to fair value accounting in an emerging economy: A case study of China's IRFS convergence” (Peng and Bewley, 2010) and “The Winding Road to Fair Value Accounting in China: A Social Movement Analysis” (Bewley et al., 2018). Design/methodology/approach This article engages directly with the arguments of the criticism. Findings This article argues that the author of the commentary misunderstands the purpose, content and findings of both papers. By providing only a narrowly focused technical analysis of the new Chinese accounting standards, the author fails to see that their qualitative research approach reveals important, complex social and political factors at play in China's attempts to adopt modern international accounting principles. The commentary expresses a view that accounting is a neutral technology that needs only to be clearly defined and enumerated to be correctly implemented, whereas this research takes a much broader and deeper perspective. The authors seek to understand how China was able to successfully adopt fair value accounting standards in 2006, whereas an earlier attempt to introduce fair value in 1998 had led to abuse of fair value measurements and the eventual repeal of fair value regulations in 2001. Practical implications This article helps clarify the purpose of qualitative accounting research, the role of theory in such research and the usefulness of theory in describing and explaining empirical case facts related to changes in accounting standards, particularly in an international context. Originality/value This article contributes to a better appreciation of qualitative 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 | 0.031 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 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".