The Evolution of Job Insecurity in Spatial Contexts in Europe During COVID-19 Pandemic
Bibliographic record
Abstract
Unemployment caused by the COVID-19 pandemic is of the utmost importance for governing bodies worldwide. Its constant increase during the last months is subject of major concern for both citizens and policy makers, as individuals might experience increased feelings of job insecurity due to the pandemic context and to the latest developments on the job market. Job insecurity refers to a perceived threat to the continuity and stability of employment as it is currently experienced and has a negative impact on the individuals’ quality of life. Many researches have linked job insecurity with low levels of well-being and high levels of stress, as well as local or national measures taken in job creation and job retention. Aside from individual factors, there are other critical influences that should be considered in order to better understand the dynamics of job insecurity against the COVID-19 pandemic. Such influences can come from regional features such as spatial, economic, or demographic characteristics, like gender, age, or education. The aim of the paper is to identify and spatially represent the variations and evolution of job insecurity during the on-going pandemic. Our analyses are based on the PsyCorona database (15.311 participants), a study with self-reported data deployed in countries all around the world, that monitored various psychological variables during the first pandemic waves. For the purpose of this research, data related to the first wave (March-July 2020) was selected. In order to get a better understanding of the spatial distribution of self-reported job insecurity, we chose to focus on eight European countries (France, Germany, Netherlands, United Kingdom, Greece, Romania, Spain, and Italy). Respondents from Western Europe countries expressed lower scores on self-reported job insecurity and less variance over time while those from Southern and Eastern Europe displayed higher scores for job insecurity and more variance. Moreover, we found that the higher the overall job insecurity is perceived in a country, the higher the discrepancies between age, gender, and education categories tend to be.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".