Atlas Unplugged: Re‐Imagining the Premises and Prospects of Capitalism for Business and Society
Bibliographic record
Abstract
Abstract Atlas Shrugged , Ayn Rand’s dystopian work of fiction, became a cornerstone of libertarian philosophy and its influence continues as an articulation of contemporary capitalism. In introducing this Special Issue, we revisit its core assumptions and contradictions in order to reimagine capitalism and reflect on the potential of management studies to contribute alternatives. These aspirations are reflected in the contributions. They discuss Indigenous views of capitalism, the ethics of care, insights from self‐determination theory, the logic of marketization, and how capitalist institutions foster violence, racism, inequality, and environmental crisis. Building from these insights, we discuss the potential for future research to draw on a combined critical lens of place and intersectionality in developing systemic analyses of capitalism. Place situates action in its meaningful social and geographical spaces, recognizing the specific historical, political, and community relations through which global forces are (re‐)produced and experienced. Intersectionality interrogates the capitalist system through various axes of identity to understand its consequences and inequalities. We use this framing to assess key aspects of contemporary capitalism: labour markets, globalization and global value chains, and access to resources. We then reflect on the prospects for alternative imaginings of capitalism and how management research might contribute to these.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".