Talk Ain’t Cheap: Political CSR and the Challenges of Corporate Deliberation
Bibliographic record
Abstract
ABSTRACT: Deliberative democratic theory, commonly used to explore questions of “political” corporate social responsibility (PCSR), has become prominent in the literature. This theory has been challenged previously for being overly sanguine about firm profit imperatives, but left unexamined is whether corporate contexts are appropriate contexts for deliberative theory in the first place. We explore this question using the case of Starbucks’ “Race Together” campaign to show that significant challenges exist to corporate deliberation, even in cases featuring genuinely committed firms. We return to the underlying social theory to show that this is not an isolated case: for-profit firms are predictably hostile contexts for deliberation, and significant normative and strategic problems can be expected should deliberative theory be imported uncritically to corporate contexts. We close with recent advances in deliberative democratic theory that might help update the PCSR project, and accommodate the application of deliberation to the corporate context, albeit with significant alterations.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.052 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".