Controversies around CSR and SD: The Role of Stakeholders in the Spiral of Hypocrisy
Bibliographic record
Abstract
Corporate social responsibility (CSR) and sustainable development (SD) controversies represent field-level contexts in which multiple stakeholders compete for moral legitimacy. Current literature has acknowledged that, in controversial episodes, stakeholders may act hypocritically, which means to strategically talk about particular subjects or embrace public positions that do not accurately represent their intentions or their concrete actions. However, in such episodes, hypocrisy has usually been seen as a circumstantial and temporally-bounded strategy, with little understanding of its consequences in terms of gaining or losing moral legitimacy in the long term. This paper analyses how hypocrisy influences stakeholders’ competition for moral legitimacy by studying the case of a controversy involving a pulp industry in South America (2005 to 2010). Following a sequence of inductive and deductive processes, we induced three hypocritical tactics (creating a pseudo-agenda, exiting the controversy, and showing autonomy) that stakeholders mobilized when competing for influence moral legitimacy and consequently the outcomes of the controversy. Our results suggest a process model which explains that when stakeholders find more useful the persistence of the episode rather than its resolution, hypocrisy spirals into the driving force of the controversy and becomes stable at the field level.
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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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".