Bibliographic record
Abstract
Purpose – The purpose of this paper is to retrospectively review what is considered to be a forgotten classic in the marketing literature, Marketing in the American Economy, published in 1952 by Roland Vaile, ET Grether and Reavis Cox. Design/methodology/approach – Marketing in the American Economy is summarized, situated in its historical context and retrospectively evaluated by the author including commentaries by other scholars today. Findings – The book’s legacy or continuing value is described as including an insightful discussion of the relative roles of the market and the state in the American economy. The closing three chapters of Marketing in the American Economy merits inclusion in any contemporary “history of marketing thought” course. Finally, Marketing in the American Economy is an early example of a textbook on macromarketing making it a significant contribution to the history of marketing thought. Originality/value – Marketing in the American Economy was reviewed when it was published in 1952. With the benefit of time passed, a more meaningful appraisal of this book can now be made with a focus on its legacy.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".