MEGAPROJECTS’ FINANCE IN THE POST-COVID-19 PERIOD: CASE STUDIES OF EURASIA TUNNEL AND YAVUZ SULTAN SELIM BRIDGE
Bibliographic record
Abstract
A new coronavirus pandemic as known as the COVID-19 impacted on every aspect of daily life. The priority of the states in the second quarter of 2020 was to protect their citizens from COVID-19 outbreak due to uncontrolled spread. The study was undertaken at a time when all the world is still struggling to take the COVID-19 outbreak under control. The states took precautions to control the virus such as restriction on travelling and curfews for every citizen or age specifically. This research is focused on the COVID-19 measures adopted by the Turkish government and their effect on the case study megaprojects’ finance at operational phases. Based on these, this research provides a new set of data to the existing body of literature on megaprojects with ‘uncertainty and finance relation’ through the COVID-19’s impact. The research methodology is based on a mixed method and multiple-case study approach with two mega projects in Istanbul, Turkey (Eurasia Tunnel and Yavuz Sultan Selim Bridge) which was financed with the Build-Operate-Transfer (BOT) method. The data consists of a detailed primary source documents and secondary data. The data is collected through the official statement of public administrations, ministries, and statistic offices. This research uncovers how the COVID-19 precautions between March 11 to May 31 in Turkey affected the megaprojects’ finances at their operational phases with an exogenous factor, COVID-19 and decrease the guaranteed incomes up to 94%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".