Investment attractiveness rating and factors affecting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aims to determine: 1) rating of investment attraction based on investor assessment; and 2) the factors which have significant effects on investment attraction of the city. Location research is in Batu city Indonesia with number of samples as 65 investors. The data analysis technique of this study uses a Multiple Regression Analysis. The independent variables used in this study are: 1) infrastructure; 2) labor availability; 3) agglomeration; 4) natural resources, 5) markets; 6) licensing system, and 7) leadership. Investment attraction is indicated with rating assessment by investors. The results show: 1) rating of Batu city investment attraction is high; and 2) licensing system and leadership have significant influences on investment attraction. Based on the result, it is very important for a city to create a conducive climate (pro investment) to attract investors, especially in the ease of the licensing system. In addition, local governments must be able to provide positive signals in the form of commitment for the investment development in Batu city. This is necessary since the city development process really needs investor support.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it