Knowledge Sharing in Organisations: Finding a Best-fit Model for a Regulatory Authority in East Africa
Bibliographic record
Abstract
Knowledge is an essential organisational asset that contributes to organisational effectiveness when carefully managed. Knowledge sharing (KS) is a vital component of knowledge management that allows individuals to engage in new knowledge creation. Until it’s shared, knowledge is considered useless since it resides within the human brain. Public organisations specifically, are more involved in providing and developing knowledge and hence can be classified as knowledge-intensive organisations. Scholarly research conducted on KS has proposed a number of models to help understand the KS process between individuals but none of these models is specifically for a public organisation. Moreover, to really reap the benefits that KS brings to an organization, it’s imperative to apply a model that is attributable to the unique characteristics of that organisation. This study reviews literature from electronic databases that discuss models of KS between individuals. Factors that influence KS under each model were isolated and the extent of each of their influence on KS in a public organization context, were critically analysed. The result of this analysis gave rise to factors that were thought to be most critical in understanding KS process in a public sector setting. These factors were then used to develop a KS model by categorizing them into themes including organisational culture, motivation to share and opportunity to share. From these themes, a KS model was developed and proposed for KS in a medicines regulatory authority in East Africa. The project recommends that an empirical study be conducted to validate the applicability of the proposed KS model at a medicines regulatory authority in East Africa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".