Turkish Shipyards During COVID-19 Pandemic
Bibliographic record
Abstract
It might be unimaginable to anticipate the pandemic but taking the initiative in measures when Covid-19 occurred was critical to success. However, the impact of this worldwide pandemic, ever known in World History, was predictable. Covid-19, the largest known pandemic in world history, will cause social, economic, and political changes. Country governments, non-governmental organizations, and companies have developed new skills and competencies to overcome the economic and social crisis caused by the pandemic with minimum damage. This paper aims to investigate the effects of the pandemic and the measures taken in Turkish shipyards in the first phase of the Covid-19 pandemic with the cooperation of GISBIR and Istanbul Technical University Academic staff. Firstly, current academic studies were reviewed by conducting a literature search. Then, GISBIR data sheets, Baltic Dry Cargo Index, shipyard order book statistics, and employment statistics were evaluated as primary data. The shipyards' websites and the sector managers' reports are secondary data. Despite the pessimistic news, academic studies, and surveys published during the outbreak's initial phase, the authors could not find any evidence to indicate the medium-term negative impact of Covid-19 on Turkish shipyards. Although the Baltic Dry Index showed a decline in the first quarter of 2020, it rose right after the second quarter, and the shipyard order book statistics increased. Employment statistics, on the other hand, continued to increase gradually. Recovery depends on the shipyards overcoming the harmful effects of the crisis by investing in continuous improvement efforts, green technology, and systems, with the support of GISBIR, giving importance to effective crisis management and distinctive ship production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".