How Did Amazon Achieve CSR and Some Sustainable Development Goals (SDGs)—Climate Change, Circular Economy, Water Resources and Employee Rights during COVID-19?
Bibliographic record
Abstract
Stakeholders’ demand for corporate social responsibility (CSR) not only creates pressure on the corporation, but corporations are also themselves aware about leading CSR activities’ reporting and embedding sustainable activities to create value for the short, medium, and long-term. This research investigates the sustainable development and corporate social responsibility of Amazon as one of the most influential multinational enterprises in the world. In this regard, this study sheds light on how Amazon has combined its own interests with corporate social responsibility and sustainable development, and how they have responded to a series of challenges brought by economic globalization to corporate social responsibility and sustainable development. The results of this detailed investigation of Amazon from 2018 to 2020 show that Amazon has performed very well in terms of social responsibility and sustainable development. In particular, climate, environment, carbon emissions and other natural measures. However, there are some shortages in terms of human rights, such as insufficient protection and care for employees during the COVID-19 pandemic, and labor union issues. In addition, the study concluded that Amazon has sufficient experience to balance profit and corporate social responsibility. In response to the challenges of globalization, Amazon has also adjusted its sustainable development strategy in a timely manner, which can be used as a reference for other multinational enterprises.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".