Paths to Work: The Political Economy of Education and Social Inequality in the United States, 1870-1940
Bibliographic record
Abstract
This dissertation examines how the expansion of formal education, so often hailed as a road to opportunity, gave rise to a new form of social inequality in the modern United States. Using quantitative data analysis and qualitative archival sources, it traces the transformation from workplace-based training for employment in the nineteenth century to school-based training in the twentieth century. This dissertation examines the city of Boston, a city that pioneered many developments in American education and was home to a heterogeneous population and diversified economy. Prior interpreters have applied competing frameworks to the relationship between education and work: “human capital” by economists, “credentialism” by sociologists, and “skill-formation regimes” by political scientists. By delving deeply into the history of this transformation, I show how an expanding landscape of schools facilitated social mobility for some, especially women and second-generation immigrants, but also encouraged “professional” strategies of job control based on exclusionary educational credentials that overwhelmingly benefited an educated, white, male, elite. My dissertation reorients the focus of contemporary inequality scholarship from the “turning point” of the 1970s to the profound transformation of paths to work a century earlier.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".