The Social Structure of Communication in Major Accounting Research Journals
Bibliographic record
Abstract
We examine the structure of communications in accounting research by analyzing patterns of citations among authors who have published in five major journals between 1984 and 2008. Understanding communication structures is important because they shape academic knowledge creation, which prominent scholars have claimed has become narrowly focused and self-perpetuating in accounting due to a specific type of communication structure - 'tribalism.' We use a mathematical algorithm and other analyses to distinguish among five types of communication structures. We find that the field contains multiple clusters, with some clusters being centered on research topics alone, a finding consistent with a 'normal academic field.' Remaining clusters are more narrowly based – on combinations of topics, methods and theory bases – and all but one of them represent a “small world” structure because they are close together and exhibit frequent communication. Both normal academic fields and small worlds have been shown to contribute positively to innovation in research. The economics-based archival financial accounting cluster exhibits some properties of a tribal structure because, while researchers in other clusters communicate toward this cluster, the cluster sends most of its outbound communication to itself. A contribution of our study is that it shows that tribalism is not as rampant as previously suggested. Also, our findings suggest the field has become less tribal over time. Further, we identify 'hub' researchers who attract communications from multiple clusters and whose articles build on, and cite, work from multiple clusters. These individuals are instrumental in moving fields away from tribalism. Finally, we discuss possible determinants and consequences of the existing communication structure.
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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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".