The Role of Accounting for Intellectual Capital in Achieving Human Resources Value in Universities Applied to a Sample of Faculty Members at the Dongola University in the Sudan & Abu Dhabi University in the United Arab Emirates
Bibliographic record
Abstract
This study aimed to identify the accounting for intellectual capital and its role in the achievement of human resources value in universities, a case study of a selected sample of faculty members of the Faculty of Economics and Administration Sciences at Dongola University and the academic programs at Abu Dhabi University. The study problem is well represented in how accounting for intellectual capital functions in its major three components, (human capital, structural capital, relational capital) and how that achieves the value of human resources in the universities. The study data has been analyzed using the Statistical Package for the Social Sciences program (SPSS). The study reached a number of results as follows: The results of the analysis of the Simple linear regression have also shown a strong correlation and statistically significant effect of overall independent study variables combined, (accounting for human capital, accounting for structural capital, accounting for relational capital) on the value of human resources in the tow universities. The study concluded with a set of recommendations, including the following: The study recommended the management of the two universities to increase the attention to the accounting for intellectual capital together with its three components (accounting for human capital, accounting for structural capital, accounting for relational capital ) by mobilizing the potential energies within the university, in a manner that enhances the value of human resources.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".