Bibliographic record
Abstract
The purpose of this research is to evaluate the social wealth and occupational priority relation with organizational effectiveness in Kerman's agricultural bank.The present study is the descriptive and correlational method and the statistical population includes all the staff of agricultural bank of Kerman for about 428 people that are determined by the use of Morgan table to 202 people.In this research, three questionnaires of social wealth with the validity of 0.92 and stability of 0.909 and the organizational effectiveness with validity of 0.92 and stability of 0.89 and questionnaire of occupational priority with validity of 0.83 and stability of 0.95 are used.The collected information is analyzed by the use of SPSS 20 software and the Spearman-Kendall correlation coefficient.The research results showed that there is relation between the social wealth and occupational priority of staffs with the organizational effectiveness.Also there is relation between the social wealth and occupational priority, so it is advised that the organizational social wealth to be improved by authorities through the correspondences strengthening and social network and group cooperation in the organization and special targets to be defined and allowed to gain the new skill and knowledge.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.924 | 0.903 |
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".