واکاوی نقش کارآفرینی روستایی در توسعه پایدار محلی، با تاکید بر پیوستگی روستایی- شهری (مورد شناسی: ناحیه دالاهو، استان کرمانشاه)
Bibliographic record
Abstract
Rural entrepreneurship can by activating innovation in the use of limited resources, lead to expansion of activities and job creation at the local level by using limited capital resources. In this regard, the people involved in this process would not only contribute to the acquisition of skills, but also to expand their business and increase the income and savings of themselves and others. Therefore, it is believed that rural entrepreneurship can facilitate effective interaction between activities related to agricultural production and non-agricultural sectors, including small industries and services, and ultimately improve living conditions and activities at the village level.This article by examining the role of rural entrepreneurship in Dalahu Area (Kermanshah Province), seeks to show that expanding these related activities can not only enhance the interaction and connection of rural settlements with urban centers, but also promote sustainable development at the local level. The statistical population of this study comprises 139 rural settlements in the area, which were examined by description- and analytic methods. To this end, library and field methods using survey and interview methods were used to collect the required data and information. Indicators measuring rural entrepreneurship and its role in sustainable local development were including the increase in employment, the increase in income levels, access to services and efforts to protect the environment. The collected data were analyzed using descriptive statistical methods and the Spss and Excel software as well as the t-test and the Pearson correlation test. The results of this study show that the Dalhousie region, despite having some environmental potential and socio-economic capabilities, due to the extreme distribution of rural settlements and their great distance from the regional center, as well as lack of easy access to commodity markets and Special mechanisms governing these markets, the activity of rural entrepreneurs has not been able to be as it should be and perhaps, with prosperity and expansion. Therefore, it can be claimed that out of 15 indicators studied, 13 indicators have a positive impact and 2 indicators have an impact In this regard, the most influences in the index of official credits granted by the Housing Bank (due to the high volume of damage in the earthquake of 1396 in the study area), government investment, access to health centers, and training center, relations It has been social and along with these factors, the most impact in this field has been related to the geographical typology index in the study area (Dalahu area).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.019 |
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 source (direct Gemma or distilled Codex), 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".