Covid-19 and Exacerbation of the Predicament of the Precariat Class
Bibliographic record
Abstract
In 2011 the British economist Guy Standing employed the term precariat, a compound word made up of proletariat and precarity, to name the contemporary underclass. There is little doubt that during the 4th industrial revolution, many people live without welfare, on minimum wages and/or with job insecurity. This workforce, often marginalized, is occupied in an array of “3D” jobs (dirty, dangerous, and degrading). Some “precariats” may have job security/ stability, but their wages are so low that their status is precarious. In this paper, we highlight that this precarity and insecurity may not be the result of lack of education, since individuals may fall victim to limited demand and huge offers concerning their skillset, which drives the competition but also leads to joblessness and nominal salaries. It is worth noting that in the last couple of years, the Covid-19 pandemic crisis has exacerbated the phenomenon. One of the reasons is that in the ensuing economic crisis, some of the entry-level jobs, filled by “precariats,” in hospitality, retail, or food services, were among the most affected by the economic recession. In the paper we explain that the trend is here to stay, even after the Covid-19 crisis has declined in intensity, due to the global financial crisis, a long-term projected recession, and the fear of a similar unexpected event on the part of employers, what some have dubbed “permacrisis.” Additionally, we illustrate that the democratic deficit of certain illiberal democracies, the necessary measures that limited the personal freedoms or even infringed on human rights in many countries to counter Covid-19. Moreover, the overall pressure of neo-capitalism might lead to rebellious reactions on the part of the precariat class, in the same way that there were revolts by the proletariat. Policy recommendations are provided as to how better deal with the issue.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".