CSR-Mainstreamed Innovation: Market Transformation for Scaled Solutions to Socio-Economic Inequity
Bibliographic record
Abstract
Corporate social responsibility (CSR) has emerged over the years as a mitigation strategy to unanticipated negative externalities of industrial technologies and markets. However, the scope, scale and impact of what has been possible through CSR in addressing these major societal challenges is clearly insufficient, as are the efforts deployed by governments and actors from the not-for-profit (NFP) sector. We develop and argue for convergent innovation (CI), a cross-sectoral approach to mainstream the societal issues targeted by CSR into core for-profit (FP) activities, placing them upfront as a driver of commercially successful technological innovation, business strategy, and market transformation, while having FP actors join governments and NFP actors to enact behavioral change and ecosystem transformation at scale. CI also entails social and institutional innovation to enable such a shift in the drivers of supply and demand at market level and in broader society. Using socio-economic inequity in access to healthy food in industrial Western society as a context, this paper lays the foundations for the dynamic modeling of equitable nutrition market transformation in the agri-food sector. We first deconstruct the existing ecosystem to specify the major inertial forces constraining change. We then use a stylized behavioral dynamic model to simulate interventions for single-actor and collective actions by FP and NFP actors and governments and examine economic change and inequity reduction outcomes over time. Results show that the economic viability of lasting social change requires cross-sectoral convergence between CSR-mainstreaming business strategy and market transformation and actions by NFP actors and government.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".