The intellectual odyssey of David D. Monieson (1927‐2008): a quest for usable knowledge
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a biographical sketch of David D. Monieson whose academic career in marketing included time spent at the Wharton School of Business at the University of Pennsylvania, the University of Toronto, and over 30 years at Queen's University. It is focussed on Monieson's contributions to the history and philosophy of marketing thought, especially with respect to what Monieson called “usable knowledge” in marketing. Design/methodology/approach This paper uses a traditional historical narrative based on extensive personal interviews with Monieson and with some of his students and colleagues as well as archival research including personal correspondence, course notes, research notes, and other unpublished documents. Findings Monieson made important contributions to the thinking about history and philosophy of marketing thought. Some of his ideas, such as the intellectualization and re‐enchantment of marketing, have found a following among marketing academics; others, such as complexity, have not. Originality/value There is no published biographical study of Monieson and no detailed analysis of his contributions to marketing thought. This biographical sketch provides insights into several significant marketing ideas and tells the life story of an important marketing scholar.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".