Living in Limbo: Exploring the Lived Experience of University Post-Graduates with Precarious Jobs in Iran and Effects on Their Well-Being
Bibliographic record
Abstract
A big population in Iran, an oil-rich country in the Global South with a huge young population, struggles with precarious employment. This is also common among those with post-graduate degrees (master’s or doctorate degrees). In this article, the researchers explore how this population experiences insecure and temporary employment with an emphasis on their well-being and social life. The researchers conducted semi-structured, in-depth interviews with 20 post-graduate degree holders in Tehran, Iran. The interviews indicated that precarious work negatively affects individuals’ well-being in many domains, including the mental health, occupational, financial, family, and social. A feeling of job insecurity, damaged professional identity, constant fear, a lack of control over one’s working life, financial difficulties, social isolation, and strained relations with families are some examples of its adverse effects on the interviewees. Women, participants from the Humanities and Social Sciences field, and those with PhD degrees reported the most damaging consequences of precarious work. To overcome these adverse outcomes, not only are individual-level strategies critical, but the government must also develop practical strategies to create more job opportunities through increased domestic production. Developing a collective agency among post-graduates with precarious work is also necessary for empowering individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".