Making sense of precarity: talking about economic insecurity with millennials in Canada
Bibliographic record
Abstract
While there are many effective metrics for quantifying economic precarity, talking to young people about their experiences in the labour and housing markets reveals a gap in explanatory language around living in/through crisis.In particular, in my research with Canadian millennials (born from the early 1980s through the mid-90s), although they could state the facts about how hard it is to get a good job or afford decent housing, what this pervasive sense of insecurity feels like is much harder to put into words.For many, a generalized sense of precariousness invades everyday life, even when work and housing are relatively secure.Thinking through this sense of anxiety, that the future might not be any better than the present and that young people might not be as well off as their parents, leads to a generational understanding of economic crisisand for a group of young adults who came of age during the downturn of 2008-2009, examining how they talk (or cannot talk) about precarity is revealing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".