Embracing Tensions in Corporate Sustainability
Bibliographic record
Abstract
Corporate sustainability is rife with tensions as firms seek to balance often divergent economic, social, and environmental goals. To assess how tensions have been addressed in past research and to identify promising areas for pushing the literature forward, we conduct a comprehensive review of research in corporate sustainability from the past 11 years. We note four general approaches to how tensions are examined: through a win-win, trade-off, integrative, or paradox lens. The win-win approach looks for opportunities to reconcile social and/or environmental goals with economic goals, thus bypassing tensions, whereas a trade-off approach views such goals as being in conflict and requires that a choice be made between them. We find that scholars have also used an integrative approach to bring balance to the three elements of sustainability. More recently, a paradox approach, which seeks to understand the nature of tensions along with how actors work through them, provides an opportunity to evaluate complex sustainability issues and generate creative approaches to them. We call on scholars to build on paradox research, which explicitly addresses tensions in sustainability, and to extend conceptual work through empirical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 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".