COVID-19, Agency and Resilience: The Experiences of Micro, Small and Medium-Sized Enterprises (MSMEs) in Ghana
Bibliographic record
Abstract
The COVID-19 pandemic that occurred in March 2020 resulted in the global economy grinding to a halt because of the various measures that were put in place by governments to stem the tide of the pandemic. While various sectors of the economy were hit hard, the micro, small and medium-sized enterprise (MSME) sector is believed to have borne the brunt of the economic shutdown that came about because of COVID. The focus of this paper is therefore to examine the experiences of MSMEs with COVID-19 in Ghana. Based on the experiences of 25 business owners in the MSME sector that were interviewed, it is argued in the paper that given that the pandemic exacerbated the challenges that MSMEs already faced, the development and implementation of various support programmes to the sector would be crucial to the economic recovery that would hopefully occur in the post-COVID period. The attempts to ensure the continued contribution of MSMEs to Ghana’s economy in the post-COVID period would be dependent on the adoption of policies that entail the provision of financial support through loans, loan guarantees and grants; and assisting MSMEs in their use of digital technology to help them with communication and marketing to their customers would be a step in the right direction.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".