An Empirical Evaluation of Accounting Graduates’ Employability Skills from Jordanian Employers’ Perspective
Bibliographic record
Abstract
In the specific case of accounting, compatibility is being sought between what accounting education provides, what accounting graduates possess and what the job market requires. Thus, from the employers’ point of view, this study will attempt to investigate the skills that accounting students have and the skills required by the labour market, and which will enhance the employability of graduates. The perception of employers of how important those skills are to the job market will be explored, as well as whether the importance of the required skills varies depending on type of sector, organisation and job position, all in the context of Jordan. In general, this study is intended to bridge the gap to the benefit of academia, professionals and students. The objective of the study has been approached by developing a questionnaire that was distributed to all major Jordanian companies and organizations in both the private and public sectors. The study concludes that accounting graduates lack, in particular, the necessary (generic) skills, and that there is a significant gap between the skills employers need and consider important, and the skills accounting graduates actually possess and can demonstrate in practice. The results show that employers tend to be more satisfied with the level of technical skills that students have acquired, than generic skills.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| 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 | 0.001 |
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".