Triple Helix Networks in a Multicultural Context: Triggers and Barriers for Fostering Growth and Sustainabilty
Bibliographic record
Abstract
This article deals with Triple Helix (university, industry and government cooperation) from an institutional theory perspective. The empirical context is the Western Cape Region in South Africa and the focus is entrepreneurship development. The purpose is two-fold: first, the existing Triple Helix model is adapted to the South African context; and second, facilities and impediments for working according to Triple Helix in South Africa are identified. The empirical material consists of a survey and three longitudinal case studies illustrating the degree of cooperation between the three parties. The article contributes to knowledge about how the Triple Helix model works on a regional level in a developing country. The study draws the following conclusions: when cooperation is to be identified between the three actors, only two of the three are involved; one missing link in the Triple Helix model is the focus on the entrepreneur; cooperation between the three parties are incidental rather than planned and there is lack of structure. In turn, some of these conclusions may be an effect of institutional changes on a national level. For a normative legacy, the article proposes a set of suggestions for incorporating all relevant parties on a practical level.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".