Exploring the Linguistic Features of Ship-Shore Communication: A Corpus-Based Multidimensional Analysis
Bibliographic record
Abstract
Investigations into maritime disasters involving human factors reveal that approximately one-third of accidents stem from communication failures, largely due to inadequate proficiency in maritime English (ME).As an important aspect of ME, ship-shore communication (SSC) is a contributory factor to the safety in the vessel traffic service (VTS) areas.While SSC instruction and learning have garnered growing attention from marine stakeholders worldwide, its specific linguistic features remain underexamined.Grounded in the multidimensional (MD) analysis framework proposed by Biber (1988), this study conducts a corpus-based comparative analysis of SSC and casual conversational English (CE) to identify how SSC varies from CE in terms of linguistic features and communicative functions.The results demonstrate that, compared to CE, SSC is characterized by greater information density, non-narrative concerns, and context independence.It tends to be less abstract and formal, less explicit in expressing viewpoints, and is produced under time constraints.Several pedagogical implications are proposed to enhance SSC instruction and improve effective communication in this high-stakes context.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".