Research the American Way: The Role of US Elites in Disseminating and Legitimizing Canadian Academic Accounting Research
Bibliographic record
Abstract
This paper examines authorship distribution in a premiere Canadian-based research journal, Contemporary Accounting Research (CAR). It provides empirical evidence of a strong US elite dominance in the research agenda of a non-US research community. This is illustrated through a consistently higher proportion of authorship representation and participation of US elites (measured by doctoral origins) in CAR and its associated Conference. We also found that a small group of Canadian schools (measured by academic affiliations) contributes the most publications to CAR but their representation on the CAR editorial board and participation at the PhD Consortium was limited. We express our concern as to the constructive role of CAR as a top-tier journal in the dissemination of accounting research. We draw upon discussion on a European research tradition (represented by Accounting, Organizations and Society and The European Accounting Review), and its general approach to accounting research, which is perceived as distinct from the US elite approach (Lukka and Kasanen, 1996). Insights gained help widen the acceptable research in top tier journals such as CAR to further its aim to enhance geographical and intellectual diversity.
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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.022 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".