Low-skill no more! essential workers, social reproduction and the legitimacy-crisis of the division of labour
Bibliographic record
Abstract
Workers in the realm of social reproduction – e.g. nurses, carers, cleaners, food preparation workers etc. – are considered low-skill and are poorly remunerated. During the Covid-19 crisis they have been recast as ‘essential’, leading to unprecedented praise and attention in public discourse. Nonetheless, public praise for these ‘essential’ workers so far has not translated into a commitment for higher wages and improved working conditions. In this article, we argue that skills hierarchies continue to determine labour market outcomes and social inequalities. We pinpoint that these are embedded into the logic of capitalist social relations, rather than being an expression of the features of jobs themselves. We also show how some socially reproductive sectors resist the tendency to automation precisely because of the prevalence therein of a workforce which is portrayed as un-skilled. By focussing on low-skilled workers’ engagement in various forms of labour unrest and their demands for long overdue recognition and wage rises. the article puts into question the inherited skills-lexicon according to which low-wage jobs are unproductive and lacking in skills and competence. The authors conclude that these workers’ fights for the recognition of the dignity and importance of their jobs and professions can facilitate a rethinking of the division of labour in our societies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.064 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".