The triple helix in developed countries: when knowledge meets innovation?
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
This paper deals with innovation viewed through the triple helix model as a milestone in the contemporary society of knowledge-based economies. Our goal is to empirically investigate the (in)efficient utilisation of academia, industry and government as three helices in order to boost innovations. Therefore, we construct a sample of 30 developed OECD countries with data covering the period from 2006 to 2018 and set up an input-oriented BCC data envelopment analysis that employs variables with non-negative average values over the entire period to calculate their efficiency scores. Our estimates from the radial models show that countries could reduce their inputs by a mean value of 11.9 per cent and keep their level of innovations in the triple helix model and by a mean of 5.8 per cent on average in the extended quintuple helix model. We find higher total inefficiencies in the non-radial models, which amount to 25.3 per cent on average in the triple helix model and 21.8 per cent on average in the quintuple helix model. The breakdown of the inefficiency score for different inputs reveals that countries have the largest potential for reducing CO2 emissions and the least room to reduce the Education Index and Civil Society Participation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it