Scoping the Evolution of Corporate Social Responsibility (CSR) Research in the Sustainable Development Goals (SDGs) Era
Bibliographic record
Abstract
Amidst a contemporary culture of climate awareness, unprecedented levels of transparency and visibility are forcing industrial organizations to broaden their value chains and deepen the impacts of Corporate Social Responsibility (CSR) initiatives. While it may be common knowledge that the 2030 agenda cannot be achieved on a business-as-usual trajectory, this study seeks to determine to what ends the United Nations Sustainable Development Goals (SDGs) have impacted CSR research. Highlighting linkages and interdependencies between the SDGs and evolution of CSR practice, this paper analyzes a final sample of 56 relevant journal articles from the period 2015–2020. With the intent of bridging policy and practice, thematic coding analysis has supported the identification and interpretation of key emergent research themes. Using three descriptive categorical classifications (i.e., single-dimension, bi-combination of dimensions, sustainability dimension), the results of this paper provide an in-depth discussion into strategic community, company, consumer, investor, and employee foci. Furthermore, the analysis provides a timely and descriptive overview of how CSR research has approached the SDGs and which ones are being prioritized. By deepening the understanding of potential synergies between business strategy, global climate agendas and the common good, this paper contributes to an increased comprehension of how CSR and financial performance can be improved over the long-term.
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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 | 0.119 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.040 | 0.054 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".