Bibliographic record
Abstract
As a synthesis of capital, technology, knowledge and information, foreign direct investment(FDI) has a significant impact on the host country's economy, and the infrastructure is no exception which is an important part of one country's economy. This paper aims to empirically analyse the impact of FDI on infrastructure, using the panel data of ASEAN countries’ infrastructure and FDI from 2003 to 2017 and compare the infrastructure effect of FDI from China and FDI from countries besides China. Result shows that FDI of ASEAN countries did improve the infrastructure level and for every 1% increase in FDI of ASEAN countries, the infrastructure level of ASEAN countries rose 0.308%. In addition, FDI from China of ASEAN countries did improve the infrastructure level and for every 1% increase in FDI from China of ASEAN countries, the infrastructure level of ASEAN countries rose 0.252%. Therefore, as a bottleneck of ASEAN's economic development, infrastructure can be improved by attracting FDI, especially FDI from China.
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 | 0.001 |
| 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".