Sustainability Performance Assessment Framework for Major Seaports in India
Bibliographic record
Abstract
In performing seaport operations, triple bottom dimensions and its related key performance indicators play a significant role in improving overall aspects of seaport sustainability. This research paper intends to examine key seaport practices that form sustainable seaport development in the Indian major seaports context from stakeholder collaboration and seaport internal sustainable management decision framework. Firstly, the key practices of sustainable seaport development were examined through a broad literature review considering sustainable seaport development and related management and stakeholder-based theories. Sustainability thematic analysis is carried out based on the identification of various dimensions and key performance indicators from various literary works. Based on the theoretical framework seaport sustainability conceptual model was developed. Semi-structured interviews were conducted with 87 seaport professionals and FAHP was performed on an input basis by 23 seaport authorities to analyze the prominence of the proposed sustainable seaport development dimensions. This study also indicated that the economic dimension is the most important, while the social dimension is the least vital dimension perceived by Indian seaport managers. This research paper will conclude with a few policy insights for seaport managers in sustainable development decisions to discover areas for improvements in maritime sustainability and enhance the seaport competitiveness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".