Assessing Employment Risk: New Dimensions in the Measurement of Unemployment.
Bibliographic record
Abstract
The contemporary era has increasingly come to be characterized as a 'risk society' (Beck, 1992).This idea underlies a growing sense of insecurity that large segments of the population feel and experience.Much of this increased insecurity is the consequence of global economic restructuring and the retrenchment of the 'social security state' (Hahnel, 2005; Eliott and Atkinson, 1998;and Wolman and Colamosca, 1997).The problem of growing economic risk was the subject of a recent International Labour Organization (ILO) report which concluded that economic security was out of reach for the majority of the globe's workers creating a 'world of anxiety and anger ' (2004a).Even in the developed world neoliberal inspired policies and restructuring have enhanced economic vulnerability as the divide between 'haves' and 'have-nots' grows (Burke, Mooers & Shields, 2000)."Work risk" is an important, if not central, facet of economic risk.We wish to address one dimension of "work risk", namely the threat of unemployment.Unemployment is important because working for pay is the primary way in which most individuals and families are able to achieve a measure of economic security and are able to more fully ground themselves in their societies thus forestalling social exclusion.However, our comprehension of the full extent of unemployment is limited by our reliance on conventional measures.High unemployment is a global issue.According to the ILO 30% of the world's labour force is either unemployed or seriously underemployed.By the end of the 1990s, there were 140 million unemployed workers worldwide (ILO, 1998: 1), and by 2003 this had climbed to 185.9 million or some 6.2% of the global labour force, "the highest 1 The research reported here was supported by a Social Sciences and Humanities Research Council grant.We would also like to thank Philip Giles from Statistics Canada for his technical support, though the interpretation of the data are those of the authors.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".