Bibliographic record
Abstract
This study was directed towards detecting the positive and negative sides for the foreign investors while they go for direct investment in India and China. A descriptive and explorative research study has been carried out for investigating the current proposition of the concerned case of FDI in those two countries. Advantages of investing in India includes-Huge market size and a fast developing economy, availability of diversified resources and cheap labour force, increasing improvement of infrastructure, public private partnerships, IT revolution and English literacy, openness towards FDI, regulatory framework, and investment protection, where as few drawbacks likes huge section of poor and middle class, bureaucracy, power shortage and ethnic diversified are also available in the country. As far as the case of China is concern positives areas are the immense size and growth of the Chinese economy and very bright prospects, resource availability and low cost of labour force, immense development in relevant infrastructure, openness to international trade and easy access to international markets, development and alteration of the regulatory framework, investment protection and promotion. There are also few drawbacks as well like the regulator burden, hindrances in free flow of information, lack of English literacy and so on.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".