Sharing Accounting Research on Equity, Diversity, Indigeneity, Inclusion, and Belonging (<scp>EDIIB</scp>): Using Infographics to Communicate Highlights and Foster Engagement<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This note introduces a set of one‐page research‐informed infographics addressing eight distinct topics on equity, diversity, Indigeneity, inclusion, and belonging (EDIIB) in accounting. The infographics serve to build community and capacity in EDIIB in the profession. This note positions EDIIB within the set of competencies needed in accounting professionals; introduces our motivations in preparing the infographics; describes briefly how the infographics may be used in accounting education, practice, and research; identifies common themes across the infographics; proposes broad areas for future research; and encourages readers to engage with the EDIIB topics addressed in the infographics and to reflect on their role in fostering inclusion within the accounting profession.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".