The framing of sustainability by different stakeholder groups in India: Perspectives, consequences and implications
Bibliographic record
Abstract
Purpose Achieving sustainable development relies on the engagement by leaders from businesses, non-governmental organizations (NGOs) and governments, particularly in emerging economies, where economic growth often creates tensions with social and environmental externalities. Therefore, our research aims to shed new light on how different stakeholders in India frame sustainability, paying attention to the congruence of diagnostic, prognostic and motivational framing tasks. Design/methodology/approach We applied a two-phase qualitative design, consisting of 32 semi-structured interviews (to generate a deeper understanding of the framings by different stakeholders) and a focus group discussion with eight key stakeholders (to validate the findings of the interviews). Findings We find two distinct framings across stakeholder groups of sustainability versus corporate social responsibility (CSR). Sustainability is framed as comprising environmental issues (diagnostic framing), requiring private sector solutions (prognostic framing) driven by the business case (motivational framing). CSR is framed as comprising social issues (diagnostic framing) that are the remit of government (prognostic framing), to be addressed through regulation (motivational framing). Practical implications Businesses and other stakeholders in India tend to view sustainability and CSR as distinct, compartmentalizing respective expenses and impact. This divergence from more holistic, international framings, while potentially reflecting local contexts, poses challenges for businesses and other stakeholders operating in the globalized arena. Social implications Legislators need to be aware of the influence of current public policy on domestic framings of social and environmental sustainability as being unrelated activities. Instead, they should consider promoting a holistic approach to sustainability, thus maximizing benefits for all stakeholders. Originality/value Going beyond prior literature, we show how actors’ frames on sustainability and CSR, respectively, are linked – in themselves but in parallel to each other – through diagnostic, prognostic and motivational framing tasks. While the two frames remain separate from each other, the three framing tasks generate a high degree of congruence within each frame; therefore, both frames have a high degree of credibility and salience. Where different framings of one and the same issue exist, individuals commonly push for framings to become either more similar to or more different from each other. In our case, neither of these happened; rather, individuals keep the tension between the two frames alive, resulting in a novel frame alignment process – which we termed frame separation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".