Sustainable Development and Corporate Social Responsibility: The Case of Corinth Pipeworks S. A.
Bibliographic record
Abstract
Investors around the world show steadily increasing support for environmental and social issues. Therefore, the majority of the companies are in favor of adopting sustainable and socially responsible actions. On one hand, Corporate Social Responsibility, according to which companies operate considering the common good of society and environment, assist in reducing environmental and social problems, but always focusing in respect of people, society and economy. On the other hand, there is the Sustainable Development, which follows the same parameters as CSR (Economy, Environment and People), with the difference that -in the light of economic growth- corporations look forward and plan their changes in order to secure their future (i.e. reducing waste, assuring supply chains, developing new markets, health and safety, etc.). In the first part of the article, both of the concepts above -namely SD and CSR- will be investigated with the aid of literature review, targeting in to not only comprehend their importance but also to recognize the changes that have occurred throughout the decades. Moreover, the article will be focused in current global standards such as GRI and ISO 26000. In the second part, through the presentation of Corinth Pipeworks S.A. case study, it will be compared how the above concepts (as well as GRI and ISO 26000) operate in a company’s real time and will be examined, how those practices have evolved in a three years’ time-period.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".