Challenges and Opportunities presented to the Albanian Economy and Labor Market during the Pandemic
Bibliographic record
Abstract
The Covid-19 pandemic highlighted many problems in the structure of the economy and hence in the employment sector, as a very delicate sector for the Albanian economy. This unpredictable factor that has affected the world economy is giving its blows every quarter to the Albanian economy. The unemployment rate has a downward trend making unemployment a major problem for the economy. The main purpose of this study is to argument and analyse the impact of the pandemic factor on the economy, and specifically the employment and unemployment rate in Albania during the economic year 2020. The period of the past year has shown how government subsidy has affected or not the reduction of unemployment as consequence of business closures. It is worth mentioning that in this paper we will try to create a comparison of two exhaustive analyses of the reports published by INSTAT and interpretations of the data of the financial year 2020. Based on these data we will create an overview of the unemployment situation and employment in general Albania, during the pandemic period. How much impact have the pandemic and the health crisis had on the economy, and what is the consequence of the pandemic in the labour market expected to continue? In conclusion, we will suggest the formulation of a group of political policies by engaging experts of various fields. These policies will not only be crucial to confront the imminent issues caused by Covid-19, but they will also be very important to overcome the general crisis created by 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".