Toward a national innovation strategy: A critique of Ghana's science, technology and innovation policy
Bibliographic record
Abstract
IntroductionThis paper takes a criti cal look at Ghana's Science Technology and Innovation (STI) Policy and argues that the conception of innovation therein is partial. Innovation basically is a new approach to resolving existing challenges or forestalling potential challenges. However, Ghana's STI policy framework largely conceptualizes innovation as an offshoot of science and technology. Although this is not necessarily wrong, it is essentially parochial as it cuts out innovations that are not based on science and technology (ST for example, in the public sector and in the domestic economy. This is because the resolution of a number of challenges in the Ghanaian public sector and the domestic economy may actually depend on the generation of new processes and other solutions that are not necessarily science-based.Even in circumstances where ST Stage 2 or the efficiency-driven stage followed by the Transition from Stage 2 to Stage 3 (Schwab, 2014: 9-11).The harsh reality of the state of innovation in Ghana is brought home more when the country is compared in the 2015 Bloomberg Innovation Index to South Korea which topped the index's ranking on Research and Development. In the comparison, it is pointed out how Ghana at the time of its independence had a GDP which was similar to that of South Korea yet lags behind South Korea by far today (Bloomberg Innovation Index, 2015). Ghana's Science, Technology and Innovation (STI) Policy cites South Korea in the same context as the Bloomberg report does and seeks to take inspiration from the Asian country (Ministry of Environment, Science, Technology and Innovation [MESTI], 2010:8).Conceptualising InnovationInnovation has been conceived in many different ways and as such, has remained theoretically ambiguous (Adams, Bessant and Phelps, 2006). This provides a challenge to the designing and implementation of innovation as it hinders a fulsome understanding of the concept (Zairi, 1994; Cooper, 1998). …
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".