Technological Changes and Employment of Older Manufacturing Workers in Early Twentieth Century America
Bibliographic record
Abstract
This study explores how technological, organizational, and managerial changes affected the labor-market status of older male manufacturing workers in early twentieth century America.Industrial characteristics that were favorably related to the labor-market status of older industrial workers include: higher labor productivity, less capital-and material-intensive production, a shorter workday, lower intensity of work, greater job flexibility, and more formalized employment relationship.Technical innovations that improved productivity often negatively affected the quality of the work environment of older workers.These results suggest that the technological transformations in the Industrial Era brought mixed consequences to the labor-market status of older workers.On one hand, technical and organizational modifications improved the elderly workers' employment prospect by raising labor productivity, diminishing hours of work, and formalizing employment relations.On the other hand, some types of technical innovations, which are characterized by additional requirements for physical strength, mental agility, and ability to acquire new skills, forced older workers out of their jobs.Since the pace and nature of technical change considerably differed across industries, and possibly across firms within the same industry, the labor-market experiences of individual older workers should have been highly heterogeneous.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".