Channels of interaction in health biotechnology networks in South Africa: who benefits and how?
Bibliographic record
Abstract
The promotion of university-industry linkages in developing countries is contested, given high levels of poverty, inequality and human development needs. A recent research trend offers new insights that can shift the terms of debate and inform differentiated policy approaches in a more contextually appropriate way. The focus is a framework to analyse the benefits and risks associated with different channels of university-firm interaction. The paper analyses case studies of diverse channels of interaction in the health biotechnology sector in six research groups based in two South African universities, a sector prioritised for its potential to enhance global competitiveness and address social problems. Analysis demonstrates the complexity of the ways in which combinations of channels are engaged in the practice of health biotechnology research groups to meet multiple economic and intellectual goals. The framework requires further refinement, but points to the importance of targeted policy attempts not only to support those channels that are likely to have the greatest benefits, but equally, to mitigate the risks of specific channels, particularly the social risks to knowledge generation and diffusion or to growing a new industrial sector.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".