Generationing relations in challenging times: Americans and Canadians in mid-life in the Great Recession
Bibliographic record
Abstract
Generation can be seen as a crossroads where multiple socioeconomic influences intersect with individual life courses. Conceptualized as a process, performed dynamically and relationally, rather than a static category, generationing builds self-identities and concepts of how the social order is expected to work. In this article the authors ask how the multilayered processes of generationing, as experienced by those in mid-life, are affected by the shock of the 2008 economic crisis in the United States and in Canada, two countries very differently touched by the crisis. The US has suffered greatly with home foreclosures, bankruptcies, continuing high unemployment and spreading poverty. Canada, by contrast, has had negligible levels of home foreclosures, few bankruptcies and lower unemployment. The data are qualitative interviews conducted specifically with those in mid-life in working and middle classes in comparable medium-sized cities in the two countries, from fall 2008 through spring 2010. The authors’ findings suggest that the shock of the economic crisis has deeply transformed the lives of those in the middle of generations and all those whose lives are linked to theirs, as well as the processes of generationing, particularly in the US, with implications for families, for societal cohesion and social order.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".