Anti-Blackness in Management and Organization Studies: Challenging Racial Capitalism in Organizing and Knowledge Production
Bibliographic record
Abstract
The papers in this special issue engage Black radical intellectual ideas to highlight the related concepts of anti-Blackness and racial capitalism. As such, these works challenge white supremacy in scholarship and beyond by providing case studies, interviews, essays, and theoretical explorations that center Black liberational thought and radical Black knowledge-making. Underpinning these efforts, is a commitment to challenge anti-Blackness in management and organization studies. Anti-Blackness is an organized and stubborn form of racism that targets Black communities by removing or denying their full humanity. In our introduction, we discuss the relationship between anti-Blackness and racial capitalism, and suggest that these are critical concepts for scholars of management and organization to meaningfully engage with. Racial capitalism has rapidly emerged over the last 10 years as a significant analytic of race and its materiality as a socioeconomic formation. We write this introduction to offer deeper insights into this concept and how its foundational ideas can be applied to current debates in the organization of scholarship, public policy, and corporate activity. Specifically, the special issue highlights the role of context and positionality in the formation of capitalism and urges scholars and activists to pay greater attention to how our analysis of race and capitalism must retain a focus on specific mechanisms and arrangements that shape these relations.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".