Social Mobility and Class Identity: The Role of Economic Conditions in 33 Societies, 1999–2009
Bibliographic record
Abstract
Using hierarchal linear models fitted to survey data from the 1999 and 2009 International Social Survey Program Social Inequality module, this article examines how social mobility shapes class identification in 33 societies. My concern is with how social mobility—both at the individual level and the country level—affects class identification. The findings demonstrate that both one’s own social class and their class origin influence class identification. On the other hand, national-level absolute mobility does not meaningfully shape class identification. This finding implies that people either consider only their own economic conditions—i.e. they care little about the conditions in which others live—or they are unaware of actual levels of mobility within their country. Finally, I build on previous research by demonstrating the importance of national-level income inequality. As income inequality rises, middle-class identities become weaker—regardless of one’s social class position—because the adverse effects of inequality are felt more acutely across the class structure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".