Bibliographic record
Abstract
Today, the issue of intellectual capital is considered one of organizational success element, and organizations need to identify intellectual capital indicators and deal with them in order to be part of the global village amidst today's highly turbulent environment.The present research explores the relationship between intellectual capital and organizational performance of Ghavamin Bank in West Azerbaijan Province, and seeks answers to the question if there is a significant relationship between organizational performance and intellectual capital improvement of governmental banks in West Azerbaijan.The research method of this study is descriptive-correlational, and applied by purpose.The study population consists of directors, top staffing expert, head, deputies, and top employees of governmental bank branches in West Azerbaijan, of whom a number of 200 individuals were chosen and 70 individuals constituted the study sample.Regarding the validity of the measuring tool, expert's opinions sufficed for the present purpose.The reliability of the intellectual capital and performance questionnaires was estimated to be 925% through Cronbach's alpha.Descriptive and inferential statistics were used for result analysis.The research hypotheses were tested by Pearson correlation, and the results of statistic tests showed that the relationship between dimensions of intellectual capital and organization performance in governmental banks of West Azerbaijan Province is significant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.971 | 0.955 |
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".