Knowledge transfer for sustainable development: East-West collaboration?
Bibliographic record
Abstract
Purpose – The purpose of this paper is to discuss the potential impact that collaboration between East and West could have on sustainable development. Greater emphasis in this paper will be placed on the benefit that developing countries gain from building collaborative relations with the West. Obtaining access to knowledge and technology will enable developing countries to speed up the process of socio-economic transformation and sustain development. Developing countries can leapfrog by making use of the existing knowledge in the West. Design/methodology/approach – This paper provides descriptive assessment of the relationship between East and West to foster growth and sustain development. The paper uses newly developed ideas to build capacity for knowledge transfer to create linkages and accelerate the process of economic growth. The approach to knowledge-based development requires the creation of an enabling environment driven by skills, innovation, institutions and ICT. Findings – The paper suggests that knowledge transfer enables developing countries to sustain development. Access to global/western knowledge allows developing countries to diversify their economic structure and increase productivity. Technological learning and knowledge absorption permit these countries to leapfrog by surpassing several stages in their development. Practical implications – Information in this paper provides insight into the merits of the new economy and the potential benefits that developing countries can obtain from participating in the global economy. Indigenous knowledge and local innovation are important for local development, which can be enhanced through technology transfer and knowledge dissemination. Originality/value – Unlike traditional economic theories in which capital and labor provide the main inputs in production, this paper discusses a new approach to development where knowledge, skills and innovation represent the main forces behind growth. The paper explores new ideas to generate linkage and sustain development.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".