Mapping Global Research on International Financial Reporting Standards: A Scientometric Review
Bibliographic record
Abstract
For the purpose to provide scholars with a more quantifiable and visualized snapshot of the realm of IFRS research (lingua franca in global business today) we conducted a scientometric review of 973 articles related to the issue published during the period from 2009 to 2020 and indexed in the Web of Science Core Collection. The findings show that the number of related articles has been increasing year by year. The global research on IFRS has been produced chiefly in the USA, England, Australia, China and Germany which not only generated majority of the high-yielding research institutions as well as productive authors but also countries of origins most of the prolific journals. Among the innumerable subject matters debated in these selected papers key are earnings management, information disclosure quality, accounting standards, the impact of IFRS, value relevance, and IFRS adoption. Since 2009, IFRS research bursts can be divided into three stages: 1) the period from 2009 to 2011 - mainly focused on the discussion of the concepts of IAS and IFRS; 2) the period from 2012 to 2014 turned to the theoretical level, and 3) from 2016 to 2020 when the research focused on the practical level. This scientometric review would complement and enrich existing literature by incorporating a quantitative perspective into it.
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 | 0.057 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.145 | 0.185 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".