Computerization and the Decline of American Unions: Is Computerization Class-Biased?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article offers a new explanation for union decline by focusing on a currently neglected site that exemplifies the fragility of unions—the shop floor in the computer revolution era. Using data from several sources including the National Labor Relations Board, it analyzes the effect of using a computer at work on the odds of being a union member and the broader effect of computerization on union strength within detailed industries between 1973 and 2002. Workers who used a computer at work were found less likely to be union members, and computerization of workplaces accounted for about a quarter of the decline in union density within industries; partly by changing the skill composition of industries’ workforces and partly by enhancing employers’ resistance to unions as measured by their use of unfair labor practices and decertification elections as documented by the National Labor Relations Board. The findings are explained in a new theoretical framework that specifies what computerization does to unions by (a) reshaping the way products are made and services are provided and (b) boosting a profound power shift throughout workplaces.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it