William Lazer: reflections on my American Marketing Association presidency
Bibliographic record
Abstract
Purpose – The purpose of this article is intended to record the author’s personal reflections on his term of office as President of the American Marketing Association (AMA). Design/methodology/approach – Personal reflections are provided in an autobiographical approach. Findings – The article discusses the AMA situation during the 1970s, membership and conferences, the Office of the President and the author’s goals and objectives as President of the AMA. Other issues discussed include certification, Canadian affiliates, the New York Chapter and how the AMA handled the Journal of Consumer Research and the Journal of Marketing during this period in time. International issues during the author’s Presidency included the International Marketing Federation, AMA’s International Activities and Strategic Plans and the Global Division. Political issues included dealing with the Doctoral Consortium, Bureau of the Census, the White House Department of Consumer Affairs, AMA Advocacy and a definition of Marketing. Originality/value – This article records events and memories that might otherwise be forgotten. No other such account has been published of William Lazer’s term as President of the AMA.
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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".