Bibliographic record
Abstract
Recent economic problems in developing countries such as uneven and unbalanced economic structure, weak capacity to attract investment and inflation prompted the methods adopted for economic growth in countries like Iran, Pakistan, Indonesia and son on to be revised.Some of these methods include the development of infrastructure, very high rates of savings and investment and increase domestic production named.But the new ideas it suggests that to improve the situation of countries, according to the private sector and expand the scope of its action, the basic points.The purpose of this research and identify factors affecting private sector failure in Bandar Khorramshahr.this study was conducted with the purpose of application and in terms of data collection and descriptive information of the type of work, the Student t-test analysis method using the SPSS software and the factors affecting successful failure the private sector at the port of Khorramshahr, identifies and reviews, and in the end the influence factors to the reduction occurred.On the research of library procedures, direct interviews, questionnaires, and the Internet to collect information and to identify the factors that influence of face-to-face interviews with experts from the public and private sectors of the two expert.The results of the research indicate that it is a failure on the political and economic obstacles to the private sector at the port of Khorramshahr was influential, in addition it was found that the performance of the private sector on the lack of success of this section on the site of the port of Khorramshahr transition impact.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.976 | 0.959 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".