The impact of the pandemic on the labour market
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
In the context of a pandemic, many enterprises take actions and make specific decisions in conditions of uncertainty,since it is absolutely impossible to predict the development of the pandemic and its possible consequences on the territory of other countries of the world. Thus, business activity also remains in an environment of uncertainty and is subject to a variety of factors that can not only negatively affect certain aspects of their activities, but can also lead to the complete destruction of the business entity. The relevance of the research topic is shown in the identification of the consequences of the coronavirus pandemic and their assessment on the modern labour market. An increasing number of employers' requirements for employees are associated with soft-skills. These include critical thinking, self-management, problem solving, learnability, resilience to stress, flexibility, and etc. The purpose of the study was to assess the current situation in the world and domestic labour market. The object of research was the labour market of the leading countries of the world: the United States, China, great Britain and Canada. The result of the study was the conclusion about further changes in the demand for labour and the conclusion about what the domestic labour market is waiting for in the future.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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