Working precariously within the social welfare system in Japan during the COVID-19 pandemic: Resilience without resistance among non-regular frontline workers
Bibliographic record
Abstract
The global COVID-19 pandemic exposed structural inequality perpetuated by neoliberalism. essential workers, including helping professionals, have experienced a high-stress level. This pilot study examined the challenges faced by social welfare workers in Japan during the pandemic. Japanese social welfare departments in municipal governments, which are primary providers of public assistance and social services, are staffed by government officers (GOs, permanent government employees) and non-regular frontline workers (NRs, hired on annual contracts, predominantly female, covering direct casework). Informed by narrative inquiries, five individual interviews of GOs and NRs were conducted. The thematic analysis highlighted the increased employment instability, individualisation, and powerlessness among NRs. NRs expressed intensified stress from the safety risk, long working hours, and insufficient organisational support. Stratified by different types of contracts, resultant tasks, and genders, NRs experienced intensified isolation, leading to burnout. The implications of working precariously in the pandemic under the neoliberal social welfare systems are discussed.
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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 | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".