Bibliographic record
Abstract
Purpose Mid-stream operation has had a significant role in Hong Kong’s economic development since the 1960s. Prior to the building of container terminals in Hong Kong, cargo was mainly loaded onto and discharged from ocean-going vessels by mid-stream operations and then shipped to Europe and North America. This paper aims to reinforce mid-stream operation is considered a “must” in supporting the substantial growth of maritime industry and strengthening Hong Kong’s role as an entrepôt. Design/methodology/approach The authors undertake a historical review of the evolution of Hong Kong’s mid-stream operation over the past half-century and investigate the future of mid-stream operation in light of the Hong Kong Special Administrative Region government’s policy of allocating Public Cargo Working Areas through an open auction process. Semi-structured, in-depth interviews are also undertaken in this study. Findings The emergence of container terminals generated competition for cargo between container terminals and mid-stream operators. In addition, the Hong Kong Special Administrative Region government’s policy of allocating Public Cargo Working Areas to mid-stream operators through an open auction process intensified negative influences on the survival of the mid-stream operation sector. Originality/value To date, mid-stream operation has been abandoned nearly everywhere except in Hong Kong. Yet, Hong Kong’s container system has become the most advanced in the world. The authors explain how and why mid-stream operation still plays such a key role in Hong Kong and how to enhance its sustainability. The authors also discuss the academic and managerial implications of their findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".