Firms, Occupations, and Markets as Tools for Combating Systemic Racism: Challenges and Opportunities
Bibliographic record
Abstract
Can for-profit firms, occupations, and markets be part of the solution to systemic racism rather than part of the problem? Past research provides considerable reason for doubt. After all, there is voluminous evidence of racial bias in the allocation of opportunities for employment and career advancement (Fernandez & Fernandez-Mateo, 2006; Pager, 2003; Pager, Bonikowski, & Western, 2009; Pager & Pedulla, 2015; Pedulla & Pager, 2019). Moreover, in Ray’s (2019) influential theory of “racialized organizations,” he argues persuasively that (American) racist schemas have become embedded in organizational rules, routines, and structures. It is in organizations (not via ambient logics alone) that schemas are fused with resource-richness. While social forces act upon the organizations (“racial superstructures”), the reverse path is also well-trod: organizations’ resources enable reinforcement of racist schemas in their own fields, markets, and wider classes, and in society writ-large - systemically (Bonilla-Silva, 1997, 2001; cf., Omi & Winant, 2014; Wilson, 1976, 1987). Ray’s (2019) diagnosis does not deny that organizations change (for good or ill) at certain times, the underlying racist schemas “remain largely stable.” For would-be reformers of for-profit firms, occupations, and markets, this account is daunting because it is a theory emphasizing major stability (amidst minor moves and counter- moves by reformers). For major change ambitions, this implies that organizations should either de-racialize or disband altogether. Yet de-racialization seems impossible given that any cultural expression in an historically racist society—including those by which organizational “spaces” are constructed—are necessarily racialized (Anderson, 2015; 2022). The recognition that it is impossible for firms to de-racialize implies that we should seek alternatives to firms. But it is notable that neither Ray (2019) nor the scholarly publications extending the theory has proposed alternatives to formal organizations as a way to better combat systemic racism. This stands to reason. Research by organizational sociologists and management theorists are unified in noting that while informal groups and networks can achieve remarkable feats of coordination and cooperation over the short term (e.g., Majchrzak, Jarvenpaa, & Hollingshead, 2007; Mollick & Kuppuswamy, 2014; Quarantelli & Dynes, 1977; Wachtendorf, 2004), however, the disquieting potential for informal groups and networks to further systemic racism exists precisely because it is hard to hold them accountable for their actions (Du Bois, 1926). The hierarchical governance mechanisms that constitute formal organizations are essential for providing the “reliability” and “accountability” for efforts that need to be sustained over time and place and in engagement with interested stakeholders (Freeland & Zuckerman Sivan, 2018; Hannan & Freeman, 1984; cf., King, Felin, & Whetten, 2010; Turco, 2016). The potential for reliability (and especially) accountability has been noted as essential (if difficult to achieve) for mitigating discrimination (Castilla, 2015; Foschi, 1996; Lerner & Tetlock, 1999). Accordingly, insofar as recent trends have witnessed a slowly “vanishing corporation” and the “uberization” of the workplace with the rise of the “gig economy,” scholars have tended to see these trends as worrisome rather than promising from the standpoint of combating (racial) injustice (Schor & Vallas, 2021). In short, accountability is essential for dismantling systemic racism and formal organizations are creatures of accountability. As such, formal organizations have the unique potential—however rarely realized—to become tools for dismantling systemic racism rather than tools for perpetuating it. This symposium showcases recent research projects focused on the social processes of inequality and interrogates the role of firms, occupations, and markets in redressing systemic racism in society. In particular, the presenters are focused on the guiding question of how firms might realize this potential, and what are the opportunities for organization theorists to provide guidance in this regard. An Organizational Dilemma: Dismantling Systemic Racism by Building Stable Racially Integrated Spaces Author: Summer Jackson; Harvard Business School Author: Ray Reagans; MIT Sloan School of Management Author: Ezra Zuckerman; Massachusetts Institute of Technology The Sources of Persistence for (Good) Diversity Programs Author: Vic Marsh; U. of Toronto, Rotman School of Management What's In It For Me? Explaining Millennials' Support for Corporate Diversity and Inclusion Policies Author: Adia Harvey Wingfield; Washington U. in St. Louis Left Behind: Affordable Housing and Communities of Color in Suburban Chicago Author: John Robinson; Princeton U.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".