Accomodation to a ”New Normality” – Risk or Benefit?
Bibliographic record
Abstract
The pandemia generated by the COVID-19 represents first of all a human tragedy, affecting society at its basis, and the effects induced by this boomerang are reflected on the labour market as well. The pandemia has accentuated the need of automation, even on the level of the insurance market, a fact that creates a lot of stress among the employees. The main purpose of the paper is to highlight the situation of the persons employed in various sectors of activity during the current pandemic conditions. The pandemic in the last year prompted large companies to explore more actively the opportunities to automate their activities. In the paper, the authors present the effects of automation on employed people in various fields of activity, including the field of insurance, which has the effect of losing jobs and replacing human staff with the assistance of artificial technology. After the implementation of automation technologies, the roles and way of working of about a quarter of employees have changed globally, while one of ten employees already needed retraining. This trend will continue to grow, with respondents stating that they will have to retrain a third of the workforce in the next three years as a result of the changing roles. The impact upon sales of goods and services is of a lasting nature and the insurance companies have to adapt their methods to reach their clients where they are, as well as in way of selling an insurance police as in ascertainment of damage and risk inspection.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".