Bibliographic record
Abstract
The devastating conditions of the Great Depression forced manufacturers to rethink their approach to workplace control, economic policy, and production practices. Although we know a great deal about how industries responded to the depression, we know very little about the changes implemented by firms. This is unfortunate as firms in the same industry face quite different problems, possess dissimilar work cultures, construct an array of production formats, and have access to a range of financial resources. Based on a literature that documents the variety of strategies devised by industries and firms, this paper shows how four Canadian textile firms—two cotton and two hosiery and knitting—reacted to the economic crisis of the Great Depression. In the face of a different array of conditions, each firm devised different restructuring strategies. The large cotton corporations responded by combining mechanization, product line change, and a new division of labor. The smaller, more competitive hosiery and knitting firms, on the other hand, imposed either a harsh regime of scientific management or conservative, piecemeal changes. In the midst of restructuring the workplace, manufacturers reasserted their prerogatives of managerial authority, selectively took advantage of the opportunities opened up by economic crisis, and created a new regime of industrial-state regulations.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".