Bibliographic record
Abstract
Abstract Purpose – To review and provide a new perspective on how Wroe Alderson contributed to marketing theory, and rekindle interest in his lines of research and the further development of marketing theory. Design/methodology/approach – A metaphor is woven into the paper to provide a new way of thinking about Alderson and his work. This provides an alternative to the more traditional analyses and comparison of Alderson's work that suggests new linkages and ways of looking at his theories, constructs and concepts. Findings – Alderson was a creative, hard working, practical marketing theorist with a drive to develop a theory of marketing. He challenged underlying assumptions of marketing, and set the discipline on a new course. Alderson himself worked on a general theory of marketing, and also inspired others to work on marketing theories. His approach and ideas still have value to today's marketing scholars. Practical implications – Marketing scholars will benefit by taking up Alderson's work where he left off, as well as integrating the research completed since his death with his theory of marketing. Originality/value – This paper uses a unique method to look at one of the key influencers of marketing; a metaphor encourages one to look at how Alderson was able to significantly impact the field of marketing, and suggests that there is still value in his work to today's marketing scholars. It also evokes ways that marketing theory can be further developed.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".