Leveraging social capital in university-industry knowledge transfer strategies: a comparative positioning framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
University-industry partnerships emphasise the transformation of knowledge into products and processes which can be commercially exploited. This paper presents a framework for understanding how social capital in university-industry partnerships affect knowledge transfer strategies, which impacts on collaborative innovation developments. University-industry partnerships in three different countries, all from regions at varying stages of development, are compared using the proposed framework. These include a developed region (Canada), a transition region (Malta), and a developing region (South Africa). Structural, relational and cognitive social capital dimensions are mapped against the knowledge transfer strategy that the university-industry partnership employed: leveraging existing knowledge or appropriating new knowledge. Exploring the comparative presence of social capital in knowledge transfer strategies assists in better understanding how university-industry partnerships can position themselves to facilitate innovation. The paper proposes a link between social capital and knowledge transfer strategy by illustrating how it impacts the competitive positioning of the university-industry partners involved.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it