Exploring the China-Pakistan economic corridor project performance during Covid-19 pandemic
Bibliographic record
Abstract
The outburst of the coronavirus into the global arena, first as a respiratory disease and later as a worldwide pandemic and health emergency, pushed the world economic order into complete turmoil and aftermath, posing severe challenges to the financial stability of developing countries like Pakistan. The temporary suspension of economic activities worldwide has resulted in significant disruptions to international supply chains, leading to substantial delays in implementing infrastructure projects associated with the China-Pakistan Economic Corridor (CPEC). The pandemic has further hindered CPEC progress. Building mega-projects, such as the CPEC, is crucial in determining the economic stability of a nation such as Pakistan. Nevertheless, it is essential to consider that the implementation of infrastructure projects can be subject to delays due to the COVID-19 lockdown and travel restrictions. However, it is worth noting that there needs to be more scholarly research available examining the ongoing progress and performance of CPEC projects from a particular perspective. This study aims to assess the impact of the COVID-19 lockdown policy and travel restrictions on CPEC project performances by highlighting the role of socioeconomic and infrastructure development factors. The study will shed light on numerous causes of concerns in project development phases and provide policy recommendations to help CPEC officials reduce project losses and better survival in the event of extreme uncertainty. The data were collected through an online survey from all over Pakistan using self-administered questionnaires with 570 responses from CPEC employees, officials, and professors from management and economic departments. The structural equation modeling (SEM) technique analyzes the problem mentioned above. As per the results of this study, it is evident that the COVID-19 lockdown policy and travel restrictions have a detrimental effect on the construction of the CPEC project. Moreover, it has been observed that the socioeconomic and infrastructure development associated with the CPEC has a notable impact on the performance of the CPEC projects. This paper aims to provide valuable insights to policymakers by examining the management of the COVID-19 pandemic from the perspective of the CPEC.
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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 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".