Investigating the Effectiveness of the Maritime Regulatory Regime to Address a Socially Responsible Shipping Industry: A Content Analysis Study
Bibliographic record
Abstract
The introduction of Sustainable Development Goals (SDGs), in 2015, has transformed the approach of public and private entities to address environmental, social and economic challenges. As result, new governance and management insights are sought, among them corporate social responsibility (CSR), which is increasingly seen as a self-regulating means to help organizations meet multifaceted challenges. With regard to shipping, global developments have called for a blueprint to facilitate industry's transition to a more sustainable pathway. However, CSR applicability in the maritime business is relatively recent and has been mainly viewed as a voluntary and beyond regulatory compliance notion. Among these shifts, this study explores the effectiveness and extent to which the maritime regulatory regime has addressed CSR topics. A case study strategy and content analysis method is employed. In turn, ISO 26000 social responsibility standard employed as the guiding paradigm to identify applicability of CSR norms within selected maritime legislation. Findings revealed a satisfactory coverage by the maritime regime of CSR issues falling under the scope of human rights, labor, the environment and organizational governance subjects. Though, it seemed to lag behind in subjects situated within the array of fair operating practices, consumer treatment and community involvement.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".