Literature Review of Management System Frameworks for CSR and Other Sustainability Concepts
Bibliographic record
Abstract
This paper presents an overview and analysis of plan-do-check-act (PDCA) based management system frameworks and other similar structured frameworks that were developed for the systematic implementation and management of corporate social responsibility (CSR), corporate sustainability (CS) and sustainable development (SD). With the aim of providing a comprehensive insight to support future research on this topic, this paper focuses on uncovering the different systematic approaches that can be adopted for the implementation and management of these stakeholder concepts at the organizational level. Our extensive literature search for articles that were published between 2000 to 2017 was able to identify only nineteen relevant articles, which indicates that there is very limited research in this field of work. Our analysis of the frameworks revealed that diverse approaches were developed for CSR. Apart from the traditional management system approach that are based on ISO 9001 (quality management standard) or ISO 14001 (environmental management standard), a variety of other approaches such as frameworks that are built on ISO 26000 (social responsibility guidance standard), organizational change management theories as well as other concepts that are similar to the PDCA cycle were developed for CSR. In contrast to the approaches for CSR, the frameworks that were developed for the implementation and management of CS or SD are mainly based on ISO 14001.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".