2023 CEIBS ESG White Paper
Bibliographic record
Abstract
In today's rapidly changing social and economic environment, marked by events such as climate change and geo-conflict, uncertainty, volatility, and complexity have become the norm in business decision-making.During this chaotic period of time, sustainability has evolved from a concept to a global consensus that must be realised.As At the same time, the rollout of new policies and improvement of disclosure and evaluation criteria have contributed to an increasingly mature ESG ecosystem.The critical challenge that companies and entrepreneurs now face is balancing business value and social value.CEIBS, a world-renowned international business school ranked among the best in Asia, is committed to the mission of educating responsible leaders versed in "China Depth, Global Breadth".With a strategic agenda of leading responsible education, the CEIBS ESG curriculum has become more comprehensive."A clearer understanding of unfamiliar concepts makes actions stronger and more targeted."CEIBS integrates ESG components into the design of teaching objectives, case development and seminars, RSLM modules, thesis and capstone projects of different programmes, so that students can better understand and reflect on ESG principles and incorporate them into their business strategy.The ESG Research Area, one of the School's four major interdisciplinary research areas, brings together the teaching and research strengths of over 30 professors from all five departments at CEIBS, with a shared goal of becoming the "go-to place" in China and Asia for the generation and dissemination of cutting-edge knowledge related to sustainability and corporate social responsibility.While steadfastly pursuing academic excellence, we have also integrated the concept of sustainable development into our long-term strategic planning and every facet of our operations.In early 2023, we began working on a green campus strategy that focuses on carbon reduction and neutrality, along with a concrete implementation plan.In the era of uncertainty, the CEIBS community has acted on its motto of "Conscientiousness, Innovation, and Excellence".CEIBS alumni and alumni companies continue to embed the concept of sustainable development into business practices, leading the transformation and upgrading of different industries through innovation in technology research and development, value co-creation among different stakeholders along the industry chain, and business model innovation.We will forge ahead with enterprise and fortitude to break new ground in ESG and sustainability research and practice.Meanwhile, we hope that the CEIBS community can make even more contributions to sustainable development!At the same time, we hope that the CEIBS community will be able to contribute more and add more value to society in a sustainable way! 01 51
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.639 | 0.636 |
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".