What's the Holdup? Sustainability Reporting's Uneven Push into Areas of “Old Guard” Traditional Accounting: A Bibliometric Analysis<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT Sustainability accounting has encountered strong headwinds while advancing into “old guard” traditional accounting. To understand the status of sustainability accounting with regard to public interest, in this paper we examine the peer‐reviewed accounting literature of the last 10 years using a robust bibliometric approach. Through co‐citation analysis, bibliographic coupling, and co‐word analysis, we find that despite the “old guard” headwinds, sustainability accounting has been progressing into traditional accounting. Disciplines that appear to intersect with sustainability accounting include assurance, taxation, marketing, and supply chain management; conversely, disciplines that lag behind include financial and management accounting. Compared to sustainability reporting and corporate social responsibility, environmental, social, and governance is less researched. The journal with the largest number of sustainability accounting articles is Meditari Accountancy Research, while Critical Perspectives on Accounting has garnered the most citations among the journals. Jan Bebbington is the author with the highest citation count. The findings of this bibliometric analysis reveal not only the status of sustainability accounting but also the “big picture” patterns in accounting research.
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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 | 0.030 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.071 | 0.143 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".