This Thing Called Goodwill: The Reynolds Metals Company and Political Networking in Wartime America
Bibliographic record
Abstract
This article examines the Reynolds Metals Company’s political networking activities in Washington, D.C., and the state capitals of the U.S. South in the 1940s and 1950s. It argues that Reynolds’ astute recruitment of senior staff from federal and state governments, its adept building of elite networks in the legislative and executive branches, its judicious espousing of key political rhetoric (antitrust, regional development, national security), as well as its nurturing of Democratic circles in the South were crucial to their attainment of competitive advantage. This saw the company rise from being a new entrant in the U.S. primary aluminum production during World War II to the second-largest national producer by 1946 and a major global player by the mid-1950s. This same political networking was critical in maintaining that advantage after World War II in the face of competition from the Aluminum Company of America and the Canadian multinational Aluminium Company of Canada. Both “wartime” (covering the period from World War II and into the Cold War) and the legacy of government intervention (from the early twentieth century until the 1960s, including the New Deal) provided a fertile context for RMC’s business strategy. The company’s success owed much to founder Richard S. Reynolds Sr.’s acumen in hiring the right people, creating or joining the right networks, having the right social capital, as well as his experiences and connections accrued from working with his uncle, the noted tobacco magnate R. J. Reynolds. The article offers insights into the nature of U.S. business–government relations.
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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 | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".