Sustainopreneurship Amongst Namibian SMEs Post-Covid-19
Bibliographic record
Abstract
Abstract The COVID-19 pandemic led to significant increase in exchange rates, price of fuel, food and shelter, as well as disruptions in education, all leading to further economical complications. Inevitably, ‘sustainopreneurship’ in Namibia was tremendously challenged as well by the turbulent times of Covid-19, and more innovative sustainable solutions will be required for the Namibian economy to develop. This chapter explores the role of sustainopreneurship as a solution to practices in Namibia. The lockdown regulations that came into effect in Namibia on 14 March 2020 have resulted in a significant increase in exchange rates, the fuel price, food prices and rent, teaching and learning in school has been severely disrupted as learners resort to online learning, health hazards and the number of fatalities has increased, while unemployment increased, resulting in food scarcity and insecurity. The experiences discussed in this chapter relate to the outcomes during and after Covid-19. The data and information for this chapter were gathered through primary and secondary sources, through focus groups and face-to-face interviews, providing an insight into the challenges faced by business owners in the informal SME sector, especially after the lockdown. The findings revealed that alternative relief measures need to be implemented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".