Impact of Engineering Insurances on the Growth of Turkish Construction Sector
Bibliographic record
Abstract
Engineering insurances are significant risk transfer instruments in terms of construction risk management. The construction sector comprises approximately 8% - 9% of the GDP of Turkey and employs 2 million people according to 2019 data. It plays a vital role in the economic development of Turkey. When the direct and indirect effects of the construction sector are taken into account, its share in the economy of Turkey reaches around 30%. Construction projects are investments that bear various risks and need meticulous execution. For this reason, it is thought that proper management of the associated risks in the construction sector by means of the risk transfer to insurance sector via engineering insurances will contribute the sustainable growth of the construction sector. In this context, the effect of engineering insurances on the growth of the construction sector is examined empirically, and positive results have been reached. The increase in the use of engineering insurance constitutes a reason for growth of the construction sector. Results of the Granger Causality test, conducted for analysis of causality, indicate that there is causality. Additionally, a mathematical model is investigated in order to observe the effect of the engineering insurances growth, on the growth of the construction sector by utilizing the simple linear regression method. In the study, the model is found to be statistically significant. As a result of the model, it is shown that the growth of engineering insurance has an impact on the growth of the construction industry.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".