Activating Balanced Scorecard Importance as a Way to Improve the Accounting Education in Jordanian Universities
Bibliographic record
Abstract
This study discussed activating Balanced Scorecard (BScs) importance as a way to improve the Accounting Education in Jordanian Universities. Data analysis was conducted using multiple regression models, a sample 134 academic staff in the Accounting departments and Managers in Jordanian Universities. The findings of regressions indicated that there is a statistically significant positive relationship between activating of BScs and improve Accounting Education, where a asserted that financial indicators, students, internal processes, learning’s and innovation contribute in performance success of accounting education, in terms of activating internal controls of all revenues and expenditures, and achievement principle of operational efficiency. It also emphasized to pay attention to students, and on providing academic services, and provide students with intellectual skills, personal, ethical, and communication with others, also surveying students, accepts complaints, continuous communication with students after graduation, meeting admission requirements for accounting students. The study also concluded that supporting scientific research culture for academics, paying attention to quality standards that deal with education and updating technology means related to teaching processes.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".