The<i>Journal of Consumer Research</i>at 40: A Historical Analysis
Bibliographic record
Abstract
This article reviews 40 years of the Journal of Consumer Research (JCR). Using text mining, we uncover the key phrases associated with consumer research. We use a topic modeling procedure to uncover 16 topics that have been featured in the journal since its inception and to show the trends in topics over time. For example, we highlight the decline in family decision-making research and the flourishing of social identity and influence research since the journal’s inception. A citation analysis shows which JCR articles have had the most impact and compares the topics in top-cited articles with all JCR journal articles. We show that methodological and consumer culture articles tend to be heavily cited. We conclude by investigating the scholars who have been the top contributors to the journal across the four decades of its existence. And to better understand which schools have contributed most to the knowledge of consumer research over this history, we provide an analysis of where these top-performing scholars were trained. Our approach shows that the JCR archives can be an excellent source of data for scholars trying to understand the complicated, challenging, and dynamic field of consumer research.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".