The Effect of Small-Scale Industry on Local Development Case Study: Karak Governorate
Bibliographic record
Abstract
The study aimed at knowing the characteristics of Small– Scale Industries SSIs in the governorate of karak and its important role in local development, Analyzing the relationship between the characteristics of SSIs that include the nature of activity, financing and location and the local development as well as the effect of this sector on the local development of Karak.A statistical package for social sciences (SPSS) program was used for descriptive analysis. All SSIs were selected for the purpose of this study.The main findings indicate that there is a high level of characteristics importance for SSIs. Financing SSIs ranked in the 1st place and location in the 2nd place followed by nature of activity in the 3rd place. There is a significant effect of the characteristics of SSIs on local development in Karak Governorate. There is a significant effect of the nature of the activity and location of the firm on local development in Karak Governorate. But there is no significant effect of finance for SSIs on local development in Karak Governorate.This study has verified further research opportunities that could enrich the understanding of SSIs in the southern region of the kingdom. The paper provides some relevant recommendations to policy makers, development agencies, entrepreneurs, and SSIs managers to ascertain the appropriate strategy to improve the SSIs sector. Key words: SSIs; SMEs; Industry; Enterprises; Local development; Jordan
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".