Precarious jobs: A new typology of employment
Bibliographic record
Abstract
work—that is, employment situations that differ from the traditional model of a stable, full-time job. Under the standard employment model, a worker has one employer, works full year, full time on the employer’s premises, enjoys extensive statutory benefits and entitlements, and expects to be employed indefinitely ( ECC 1990; Schellenberg and Clark 1996; Vosko 1997). Work that differs from the standard is described in several different ways, ‘non-standard ’ and ‘contingent ’ being two commonly used terms. Non-standard is used widely in Canada (Krahn 1991, 1995), contingent in the United States (Polivka and Nardone 1989; Polivka 1996). Another approach is to consider dimensions of ‘precarious employment ’ in relation to a typology of total employment (Rodgers 1989; Fudge 1997; Vosko 2000). Many non-standard jobs may correspond to an employee’s life-cycle needs—such as combining part-time work with full-time education, or devoting more time to activities outside the workplace. Indeed, men’s and women’s differing reasons for part-time work and self-employment illustrate the importance of gender-based1 analysis of trends in non-standard work. For example, in 2002, 42 % of men compared with 25% of women worked part time because they were attending school, while 15 % of women and just 1% of men cited child-care responsibilities. These findings reflect differing care and education trade-offs for men and women (see also Vosko 2002). At the same time, slightly over one-quarter (27%) of part-timers were working part time because of poor business condi-tions or because they could not find full-time work. The 2000 Survey of Self-Employment also highlighted differences in self-employment patterns for men and women. Data indicated that 13 % of own-account
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".