Bibliographic record
Abstract
An Introduction to the Study of Careers and their History: The history of the modern career: an introduction, John Brown, Marco van H.D. Leeuwen and David Mitch An economic perspective on career formation, Laura J. Owen The use of event-history-analysis in career research, Ineke Maas. Part I: The Creation of Formal and Informal Structures: Constructing the modern career, 1840-1940, Andrew Miles and Mike Savage Job stability and career opportunities in the work-life history of policemen in Victorian and Edwardian England, Haia Shpayer-Makov Trade unions and employment stability at the Canadian Pacific Railway, 1903-29, Mary MacKinnon Career making at Pullman: employment stability and job mobility for railroad repair shop workers, 1915-1970, Susan Eleanor Hirsch and Janice L. Reiff Skilled work and labour careers in the Argentine printing industry, 1880-1930, Maria Silvia Badoza. Part II: The Influence of Gender: Small business, self employment and women's work-life choices in 19th century London, Alison Kay The careers of female graduates of Cambridge University, 1920s-1970s, Pat Thane. Part III: The Influence of Industrialization and Economic Modernization: Occupational careers of the total male labour force during industrialization: the example of 19th century Sweden, Ineke Maas and Marco H.D. van Leeuwen Working class careers: on-the-job experience and career formation in Munich, 1895-1910, John C. Brown and Gerhard Neumeier Agricultural labour as a career: Norfolk farm workers in the late 19th and early 20th centuries, David Mitch Factory work and arrangiarsi alla canesca. Professional careers of the Saffat's steelworkers from 1900 to '29 crisis, Paolo Raspadori Career patterns in the British chemical profession during the 20th century, Robin Mackie and Gerrylynn K. Roberts Index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".