Knowledge Dissemination and Exchange of the Newly Created Knowledge
Bibliographic record
Abstract
As a researcher, it is important to plan how you are going to disseminate and share newly created knowledge by research at the initial stage of your research project. There will be no use or value of your research project, even though it is a very prominent one scientifically, if you fail to disseminate and share the newly created knowledge properly. Therefore, any researcher must pay high attention and give priority to the way of dissemination of newly created knowledge through a research project. According to the Canadian Institutions of Health Research, dissemination processes and approaches should be informed by high-quality context of specific evidence, user driven knowledge, ways to measure success and plan to evaluate the impact of the chosen approach. Also it is important to consider what the message is about, who the audience is, who the messenger is, what the best dissemination method is and what the expected outcome is. Knowledge transfer (KT) is to comprehend a very broad range of activities therefore, to support mutually beneficial collaborations between universities and research institutes, business world and the people of the communities. Further, KT is all about the transfer of tangible and intellectual property, expertise, learning and skills between academia and rest of the community. Therefore, knowledge dissemination means to make the knowledge accessible and usable. In such context, the importance of research journals is towards its major role of knowledge dissemination, especially among the academia, research personals, and in some occasions, the business community and policy makers. The Journal of Agricultural Sciences has been popular among the academia and research personal during the past decade and it has fulfilled its major role of knowledge dissemination. Further, knowledge diffusion can be defined as the adaptation and application of knowledge documented in scientific publications such as science journals, conference proceedings, monographs, etc and patents. It is apparent that this kind of knowledge diffusion is mainly done by research and academic journals. At present, the JAS has been included in more than 35 journal databases including AGRICOLA, AGORA, Cabi Abstracts, TEEL and DOAJ. It shows a wider acceptance of JAS by international citations and journal databases. This is an indicator for our progress in journal quality during past years. Many personages have stood behind this success. The honour must be extended therefore, to Dr. Chandrika Dissanayake - Coordinating Editor of JAS, Mr Prasad C. Iddamalgoda, Ms Sioux Cumming and Andy Nobes of SLJOL, all the authors, reviewers and members of the editorial committee.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".