The Evolution of Accounting Science: COVID-19 Pandemic Lessons on Anti-Black Racism
Bibliographic record
Abstract
The United States adopted a US-first vaccine policy and withheld vaccines from Canadaits most important trade and foreign partner.Human instinct in its most primal form is insular and tribal.2 EDI is the terminology used in Canada.In the United States, EDI is referred to as diversity, equity, and inclusion (DEI).born in the United States on October 14, 1973, the week after I was born in Nigeria.We were essentially born at the same time, in different places.I conduct research at the intersection of business and racism, and he lost his life at the intersection of business (he allegedly passed a counterfeit $20 bill) and racism.My social awakening led me to a research program on discriminationparticularly anti-Black racism but also racism more broadly as well as sexism, and nameism.Coincidentally, in fall 2019, along with three other Black scholars, I had commenced a structured literature review (SLR) titled "A Knowledge Synthesis of Anti-Black Racism in Accounting Research" (Ufodike et al., 2023) for a special issue of Accounting Perspectives on literature reviews.We had no idea what was coming in the spring, but by May 25, 2020, when Floyd was murdered, the relevance of our study became more apparent and easier to situate in the literature.Our study, published in summer 2023, is the first knowledge synthesis on anti-Black racism in accounting literature of which I am aware.Our objectives were to identify and summarize extant accounting studies on anti-Black racism and to propose avenues for future research.We found only 25 related studies, including work from Anton Lewis (2015, 2016), Theresa Hammond (1997, 2003), Cheryl Lehman (Lehman et al., 2016), and Ida Robinson-Backmon (Robinson-Backmon et al., 1997).The scarcity of accounting studies on anti-Black racism is partly the result of the torturous path to tenure for those who undertake this work, and perceptions in accounting that the issue of racism belongs elsewhere, such as in human resources (Lewis, 2016).Prior to 2020, my body of research focused primarily on public accountability (Ufodike, 2017, 2020), network accountability (Ufodike et al., 2021, 2022), and public sector financespecifically P3s (Opara et al., 2021(Opara et al., , 2022)).Floyd's murderan explicit example of anti-Black racism exacerbated by the COVID-19 pandemicmarked a turning point in my research, and I started studying race and discrimination in and by accounting.The SLR was my first project on race and discrimination in and by accounting.During the SLR study, I observed that the United States and South Africa were the primary sources of the literaturean unintended but notable consequence of both countries' ugly pasts with anti-Black racism.In Canada, the literature was nonexistent, as was racial data collection by universities and the accountancy profession (CPA Canada) (Ufodike et al., 2023).The limited works that remotely concerned the Black experience mainly examined labor market integration of immigrant accountants more broadly (primarily from India) and were conducted by the duos Kelly Thomson and Joanne Jones (2016) and Marcia Annisette (2003) and Umashanker Trivedi (Annisette & Trivedi, 2013).Having demonstrated the gaps in the accounting literature (Ufodike et al., 2023), I then also responded our own call for future research and decided to expand the study to a comprehensive projectonce again, the first of its kind in Canada, perhaps in any of the Organisation for Economic Co-operation and Development (OECD) countriesto investigate the barriers and challenges that prevent Black people from entering or thriving in the accountancy (and any business) profession.In fall 2021, I applied for a grant to the Social Sciences and
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".