A study on the effect of social capital on learning organization: A case study of Jihad Agriculture Organization of Kermanshah, Iran
Bibliographic record
Abstract
One of the challenges in contemporary zone of management and organizational behavior is to create and strengthen social capital.Social capital arises from individuals trying to help people build trust, relationships and cooperation.Without social capital, employees are not able to share information and knowledge.The purpose of this study is to review the relationship of social capital and the learning organizational in one of Iranian organizations called Jihad Agriculture Organization of Kermanshah.The statistical population includes 270 employees of this organization and a sample size of 159 people are chosen using Morgan statistical table for a the first six months of 2012.The research method is descriptive-survey, the type of correlation and a questionnaire for collecting information are used.A number of university professors confirmed validity of the questionnaires.Their reliabilities were obtained with Cronbach's alpha and the coefficients for questionnaire of social capital was 0.705 and for questionnaire of learning organization was 0.838, respectively.By using correlation coefficient and multiple regressions, the data were analyzed.Results in a significant level of 95% showed, social capital had a meaningful relationship with learning organizational.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".