The Evolution of Accounting Science: COVID-19 Pandemic Lessons on Anti-Black Racism
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,008 | 0,006 |
| Études des sciences et des technologies | 0,006 | 0,036 |
| Communication savante | 0,016 | 0,021 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,006 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».