An Analysis of scientific research in the field of international financial reporting standards: A Scientometric study
Bibliographic record
Abstract
The significance of International Financial Reporting Standards (IFRS) and the interest of a diverse array of global researchers underscore the globalization of financial activities. Despite the substantial volume of publications in this area, the intellectual framework of researchers remains largely unexplored. This research aims to clarify the current state and evolution of this field, highlight the intellectual structure of researchers, and provide new insights for future researchers. The research approach employs scientometrics and data mining. All studies indexed in the Scopus database were analyzed. Word co-occurrence, co-authorship analysis, and Latent Dirichlet Allocation were utilized to create a knowledge map. The co-authorship network analysis by country revealed that the United States, England, Australia, Germany, Italy, Canada, Spain, France, and China exhibited the highest centrality ratings. This research also explored trends and knowledge frontiers over different periods within this scientific domain, which can be categorized into themes such as the acceptance and implementation of IFRS, standard-setting and regulatory frameworks, the quality and transparency of financial reporting, the implications of international standards, corporate governance, and IFRS. The findings provide researchers with a clearer understanding of the existing literature by examining the current state of research in this field. This insight enables them to implement innovative strategies to advance and further develop the scientific discipline. Additionally, researchers can identify potential areas for future studies and interventions by gaining a deeper understanding of the concepts and methodologies within this field.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.029 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.023 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".