Millennials in Canada: Young Workers in a Challenging Labour Market
Bibliographic record
Abstract
The cohort of young workers born between 1980 and 1995 has been given a wide range of labels by various authors and commentators, including “Millennials” (Strauss and Howe 1991), “Generation Y” (Johnson and Johnson 2010),“Gen Me” (Twenge 2006), “Nexters” (Zemke et al. 2000), “the next great generation” (Howe and Strauss 2000), and the “nexus generation” (Barnard et al. 1998). In recent years, they have earned an unfortunate new moniker: “generation screwed” (Girod and Shapiro 2012). This epithet conveys an image of a generation facing an uncertain future with bleak prospects of quality permanent employment, rising levels of personal debt, and an inability to maintain the quality of life afforded by their parents (Carbone and Cahn 2014). The current narrative in the mass media and popular press suggests that despite high levels of education and technological skills, Millennials across the developed world are plagued by high levels of unemployment and underemployment (Foster 2012). If this characterization is correct, persistent high unemployment rates could create social and economic problems such as long-term (structural) unemployment, widespread low-quality jobs, and a loss of confidence among young workers (ILO 2013). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".