Corporate Constructed and Dissent Enabling Public Spheres: Differentiating Dissensual from Consensual Corporate Social Responsibility
Bibliographic record
Abstract
I here distinguish dissensual from consensual corporate social responsibility (CSR) on the grounds that the former is more concerned to organize (or portray) corporate-civil society disagreement than it is corporate-civil society agreement. In doing so, I first conceive of consensual CSR; and identify a positive and negative view thereof. Second, I conceive of dissensual CSR, and suggest that it can be actualized through the construction of dissent enabling, rather than consent oriented, public spheres. Following this, I describe four actor-centered institutional theories – i.e. a sociological, ethical, transformative and economic perspective respectively – and suggest that an economic perspective is generally well suited to explaining CSR activities at the organizational level. Accordingly, I then use the economic perspective to analyse a dissent enabling public sphere that Shell has constructed, and within which Greenpeace participated. In particular, I explain Shell’s employment of dissensual CSR in terms of their core business interests; and identify some potential implications thereof for Shell, Greenpeace, and society more generally. In concluding, I highlight a number of ways in which the present paper can inform future research on business and society interactions.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".