TECHNOLOGY ADOPTION: A SOLUTION FOR SMES TO OVERCOME PROBLEMS DURING COVID- 19
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
Unfortunately, SMEs expect to provide a significant share in the economic growth of the nations, but the organizations are facing the problem of resource limitations Most of the consumers were spending their disposable income on buying products, but due to COVID-19, most of the consumer is facing a job loss or salary cut, so the spending power is decreasing In the United Kingdom, 69% of SMEs are facing severe cash flow problems with 35% are facing the fear of not reopen again Petropoulos, (2020) ;in China, 80% of SMEs have stopped their operation during February' 2020;in the United States 70% of SMEs are expecting disruptions in supply chain nearly 80% SMEs are facing destructive impact directly or indirectly (OECD, 2020);78% of Canadian SMEs have reported a drop in sales;in Greece, SMEs have experienced 60% decline in sales;in Thailand, 90% of SMEs are expecting drop in revenue;in New Zealand, approximately 71% SMEs have experienced gain hit, and in India, cash situation is awful for SMEs (OECD, 2020) [ ]they are not ordering the products in Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it