Bibliographic record
Abstract
Purpose By tracing something of my history in becoming a marketing professor and conducting research, I hope to demonstrate that if I can flourish without compromising my ideals or losing my enthusiasm for research, so can anyone. Design/methodology/approach I attempt to show my trajectory, emphasizing the little turns along the way that have sent me in certain directions. I also discuss several of my most cited articles and discuss how they emerged. Findings I emphasize reading broadly, working with interesting co-authors, learning from my students and breaking free of narrow disciplinary boundaries as ways that have helped to stimulate and inspire me. The implicit message here is to take a chance and dare to do something different from what seems to be the mainstream. Practical implications There is certainly no single way to pursue a successful career. But I hope that by showing how I happened to get to where I am, I may give support and encouragement to those who may feel they are outside the academic mainstream in marketing or that their ideas don’t quite jell with those of colleagues. Originality/value The story and opinions here are mine alone. Together with others’ biographies and autobiographies (Shaw and Wilkinson, 2011), hopefully the reader can find a range of possibilities.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".