The Extent to which Jordanian Organizations Are Committed to Implementing Knowledge Management According to Jordanian Government Standards
Bibliographic record
Abstract
The study aimed to identify the extent to which Jordanian organizations are committed to implementing knowledge management according to Jordanian government standards, and the extent to which knowledge management standards are applied in Jordanian organizations. To highlight the problem of the study, the sub-criteria for knowledge management were discussed, which includes the ability of the organization to: the commitment of senior management to knowledge management and awareness of its importance, knowledge resource identification, knowledge management and knowledge outcomes standard. The study community is from all organizations and directorates in the Kingdom of Jordan. The study sample consists of (85) organizations and directorates, who all applied for the King Abdullah II Award for Excellence in Government Performance and Transparency. The results of the study showed that Jordanian organizations are not committed to the standard of commitment of senior management to implement knowledge management, and the lack of commitment of the Jordanian organizations to the criteria of the main performance indicators to measure the achievement of knowledge objectives and knowledge exchange indicators, while there is a commitment in the Jordanian organizations to the standard of knowledge management. The most important recommendation of the study is the need to develop a comprehensive strategy for the application of knowledge management within the organization; the study recommends further studies in the area of knowledge management since it is a new management concept.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".