Corporate Social Responsibility: Preserving Neoliberal Capitalism or Prefiguring Alternatives?
Bibliographic record
Abstract
My argument in this paper has two parts. The first is that Corporate Social Responsibility (CSR) emerged as a one of global business’ preferred strategies for quelling popular discontent with corporate power. But the second part of my argument is that CSR has taken on a life of its own and represents an enabling shift in how business is being understood. Primarily I argue that CSR discourse has accelerated the development of alternative business forms that prioritize sustainability and social justice more than maximizing profit (i.e. social business and the larger social economy). While I think CSR discourse developed to quiet public concern with corporate power, it is not clear where it will end up. The political world is a complex place where good intentions can result in bad outcomes and bad intentions can result in good outcomes. As Max Weber argued in his essay ˜Politics as a Vocation it is not true that good can follow only from good and evil only from evil, but that often the opposite is true. Anyone who fails to see this is a political infantâ€? (1969, p. 123). Perhaps the lip-service corporations are paying to sustainability means more effective regulation will continue being forestalled in the short term. But it is also possible that CSR discourse has helped spark an economic transformation that prefigures alternatives to neoliberal capitalism--something the original proponents of corporate social responsibility might not have counted on.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.055 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".