Conceptual framework for corporate governance in crisis period on example of hospitality industry in Latvia
Bibliographic record
Abstract
The pandemic caused by COVID-19 has caused a crisis in all spheres of life, there are many restrictions, including on crossing national borders and moving within one country. To limit the spread of the pandemic, many international measures have been adopted that have had a far-reaching, even catastrophic impact on tourism and the hospitality business. Many hotels around the world are in danger of bankruptcy. The topic of this study: impact of COVID-19 to the hospitality industry in Latvia and related crisis management measures. The question of the study: how does spread of COVID-19 pandemic impact hospitality industry in Latvia and how to evaluate crisis management measures implemented in that area of business with an aim to minimize mentioned impact? The authors have analyzed the conceptual foundations of anti-crisis management in general, taking into account the current situation in the hospitality industry of Latvia in the context of the crisis caused by COVID-19 in particular. All market economy actors operating at the macro and micro levels, depending on the phase of the economic cycle, use different systems and strategies to ensure their sustainability. So A. Belyaev in the context of the crisis considers management as a process of financial recovery of the company, i.e. the author considers this process only at the level of an economic unit (micro level). This definition is flawed, since the author narrows the concept of crisis in it, without indicating the reason for its formation, describing in detail the process of recovery of the company. The purpose of the described by authors algorithm is to step-by-step optimize the actions to overcome the crisis at the macro level, which then reduces the probability of its occurrence and the severity of the processes taking place in it.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".