Bibliographic record
Abstract
Hostility to trade unionism infused the corporate culture of Walmart, whose revolutionary transformation of the retail industry in the United States made it, for a time, the nation’s largest and one of its most dynamic companies. A product of the rural South, Walmart required an ideologically sophisticated strategy to stymie union organizing when it began to open its stores in metropolitan America. Employment lawyer John Tate proved his worth in this regard. The anti-union strategist’s ideas had been shaped by his clients, some of the most intransigent anti-union employers of the textile South and the small-town Midwest. In the early 1970s, Tate codified for Walmart and other service-sector firms the key elements of a successful union avoidance strategy: construct a business ethos that mimics the sense of community found in monoracial, small-town America; deploy a pension scheme that rewards loyalty for a core of longtime employees; and use the “free speech” rights of employers to paint unions as corrupt and self-serving institutions incapable of improving the wages or working conditions of employees. He proved to be successful: there are no unions at any of the 5,000 plus Walmart stores in the United States and Canada.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".