Bibliographic record
Abstract
Employment in the U.S. textile and apparel industries has been declining for more than a quarter of a century. Employment reached its peak in 1973, and since then it has declined by 57 percent in textiles and 63 percent in apparel through March 2002. Total employment also decreased in the steel and automobile industries and in the broader manufacturing sector over the same period. These employment figures from particular industries and a single sector of the U.S. economy might leave the mistaken impression that the entire U.S. economy has been shrinking. On the contrary, this extended period was one of extraordinary prosperity in which total employment in the country grew by 71 percent, worker productivity (including textiles, steel, and autos) grew by 57 percent, and income per capita grew by 72 percent. Declining employment in certain traditional industries did not prevent increasing affluence for the average American. These contradictory employment experiences for textiles, steel, and autos and for the general economy represent the forces of what Joseph Schumpeter (1934) called “creative destruction. ” Innovations that stimulate general economic growth simultaneously destroy specific jobs as emerging technologies replace older technologies. Creative destruction has gotten more attention recently because it is a major component of globalization, and many prominent job losses have been attributed to import competition. During this period 1.5 million jobs were destroyed in textiles and apparel, but total employment in the economy grew. For each textile job eliminated, 36 more jobs were created in other industries. Employment in the U.S. steel industry declined by 361,000 during the period, but more jobs were created elsewhere. The new jobs created did not all require the same skills or have the same location as the old
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".