The Roles of Personality in the Context of Knowledge Sharing: A Malaysian Perspective
Bibliographic record
Abstract
Our era of knowledge today has shown the fact that accomplishments achieved by public agencies receive influence from several inside factors; to name but a few, individual intelligence and personality characteristics. The emphasis on this paper rests on the contributions made by personality and the act of implicit knowledge-sharing to improve individual presentation, with special regards to managers in the public sector. There is a potential that this paper can serve to justify how individual differences are able to weave their way among knowledge workers for performance improvement. The assessment of personality traits is performed using the Big Five Inventory, where the traits are extraversion, agreeableness, openness to experience, neuroticism and conscientiousness. Tacit knowledge sharing, on the other hand, is realized through mentoring and knowledge-sharing agenda. This current study is targeted at Malaysian public sector managers who are expected to distribute their valuable knowledge with others and help enhance individual productivity. Individual performance system is introduced as a measuring tool on individual productivity which comprises of four main components; knowledge and expertise, personal quality, leadership and community contribution. It is suggested that for the purpose of practising knowledge sharing, managers need to possess some personality traits to improve their employees’ performance.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".