PERCEPTIONS AND EXPECTATIONS OF AFRICAN HIGHER EDUCATION STUDENTS ABOUT BIOECONOMY
Bibliographic record
Abstract
Bioeconomy offers new approaches to dealing with environmental challenges, such as replacing fossil fuels with sustainable and renewable resources and fuels. So, knowing how African higher education students perceive Bioeconomy is important. To achieve this objective, an exploratory and quantitative study based on a convenience sample of 407 students was conducted. The data was collected using an adapted questionnaire and analyzed using descriptive statistics. The majority of students came from rural areas (88.0%), were female (56.’%), and were between 18 and 22 years old (52.3%). Students favor replacing fossil resources with renewable ones so that the transition to a sustainable Bioeconomy can be achieved (89.2%). The three main measures to be implemented if students were responsible for preparing a transformation plan for the Bioeconomy, are: (1) improvement of knowledge and information about Bioeconomy; (2) improving the participation of the population and companies in the transformation process; (3) developing of recycling and reuse of materials. Furthermore, the majority of students consider that the transition to the Bioeconomy could reduce the amount of plastic waste in the environment and oceans (67.1%), the loss of natural environments (55.4%), the emission of carbon (54.1%), species extinction (52.6%) and particle pollution (52.3%). Regarding students' perceptions about the beneficial contributions that Bioeconomy can provide, in economic, social, and environmental terms, students believe that Bioeconomy is capable of promoting major improvements, namely, creating new jobs (78.1%), achieving a more sustainable international development model (68.1%), improving access to new areas of research and education (64.6%), improving economic performance and regional and international competitiveness (63.6%), reducing dependence energy (58.7%) and ensuring the security and stability of the energy network (56.5%). This study also shows that students do not consider themselves adequately informed about pertinent topics related to the Bioeconomy since only around 27% reported being quite familiar with the sustainable development objectives defined by the United Nations. Furthermore, topics such as genetic engineering in agriculture, the cultivation of energy crops, and the digitalization of agriculture are only familiar to just over 10% of respondents. When asked which sources of information about the Bioeconomy they trust most, environmental and farmer organizations stand out positively, and national and local governments negatively. Regarding pro-environmental actions carried out by students in the last 12 months, the majority highlights the conscious purchases of regional food products (63.4%), neglecting other types of equally important actions, namely, the change in mobility behavior, the abandonment of packaged products, the use of renewable energy, the purchase of green products, among others. Finally, concerning interests and values, students believe that it is possible, by managing resources well, to avoid environmental catastrophes even though they realize that the environment is very fragile and that any human interference can result in devastation. Perhaps for this reason, the majority are not defenders of the free market and economic growth at the expense of the environment. There are gaps in understanding the advantages, visions, and topics associated with the Bioeconomy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".