The Social Contract Between CSR and Social License: A Sentiment and Emotion Analysis
Bibliographic record
Abstract
While prior literature has assumed that companies engaging in corporate social responsibility (CSR) can always gain social license (SL) from local communities, this study examines the boundary conditions of this assumption. Using justice-based and consent-based social contract theory, we theorize that CSR at the global and local levels improves the degree to which a local community grants a SL to a multinational company (MNC), but this degree is negatively moderated by the diversity among stakeholder groups within the community. we build a unique dataset from 3,190 news articles describing the SL of 43 mining MNCs operating in 456 local communities between 2005 and 2019. Considering SL as a continuum grounded on such emotions as trust and anger, we use natural language processing and big data techniques to measure SL through a sentiment and emotion analysis. Findings of this study show that CSR at the local level is more significant in increasing the degree of SL than at the global level, and that community diversity negatively moderates the relationship between CSR at the local level and the degree of SL. This study thus reveals that local communities tend to have a consent-based approach towards the social contract between CSR and SL.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".