Talent Management and its Relationship to Career Stability among Academic Leaders at Al-Hussein Bin Talal University
Bibliographic record
Abstract
This study aimed to identify the level of applying talent management and achieving career stability among academicleaders at Al-Hussein Bin Talal University (AHU), and to investigate the relation between talent management and itsdomains with career stability. The study population consisted of all the (55) administrative academics in Al-HusseinBin Talal University occupying positions: Dean, Vice Dean, and Head of Department. The analytical approach wasused to develop the study tools and the descriptive approach to determine the level of applying talent managementand the degree of career stability. To achieve the study objectives, two tools were used: applying talent managementand achieving career satisfaction where its validity and reliability calculated. The results showed that means fortalent management ranged between (3.236-3.448) with medium agreement; the highest among them was the domainof (talents acquisition), followed by the domain of (talents development), while the lowest was the talentsmaintenance domain. The total mean was (3.344) with a medium degree of agreement. The results also indicated thatthe means for the items of achieving career stability ranged between (3.218-4.273) with high and medium degrees ofagreement, and the mean for the total was (3.82) with a high degree of agreement.The results showed that correlation coefficients between the talent management and the career stability rangedbetween (0.685-0.799), which indicates that there is a positive relationship between them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".