How the Emphasis on ‘Original’ Empirical Marketing Research Impedes Knowledge Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Empirical research in marketing should focus on the development of empirical generalizations. Marketers do a huge amount of empirical research, but have little in the way of empirical generalizations. This is primarily because most empirical research consists of ‘original’ or ‘novel’ works looking for significant differences, rather than significant sameness, in unrelated data sets, thus exemplifying the ‘cult of the isolated study’. As a result, the marketing literature is made up largely of uncorroborated, fragmented, ‘one-off’ results. Such results are of little use to marketing practitioners or academicians. We discuss a number of impediments to the development of empirical generalizations – preoccupation with the hypotheticodeductive conception of science, preoccupation with ‘statistical’ rather than ‘empirical’ generalization, the ‘publish or perish’ syndrome in academia, and denigration of replication-with-extension research. We conclude that replication-with-extension research must be championed as the vehicle for discovering empirical generalizations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it