From Fragility to Resilience—How Prepared Was the Romanian Business Environment to Face the COVID-19 Crisis?
Bibliographic record
Abstract
The issue of business resilience is a topical one, in the context of which a large number of the companies on the market have faced many challenges in the last two years, raising the issue of market survival. But was the Romanian business environment ready to face the COVID-19 crisis? How prepared is it to continue to face the obstacles posed by the pandemic? The purpose of this paper is to identify the main effects that the pandemic has generated on Romanian SMEs, while presenting the results of a study on this topic. We proceeded to determine the level of familiarity of respondents on the evolution of SMEs during the pandemic and the study of their level of interest, on the effects generated by the current epidemiological context, and on the evolution of the business environment, while analysing the level of optimism/pessimism of the respondents, regarding the general evolution of the Romanian business environment. Although some companies are open to implementing change, there is also a significant percentage of firms that, for various reasons, do not consider making major changes in the near future, either because they are not aware of the need for change or out of fear.
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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 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".