Rational and Emotional Advertising: A bibliometric Analysis (19902020)
Bibliographic record
Abstract
This study analyzes trends in the scientific literature on the concepts of rational and emotional advertising. The article presents a bibliometric analysis of 96 studies on rational and emotional advertising, taken from the Web of Science database (WOS) for the period 1990-2020. The study categorizes these documents according to bibliographic indicators, i.e., most productive authors, year of publication, countries with the highest productivity rate, the journals and universities that published the most on this topic, language, type of research and field of research. This analysis provides an overview of the nature and trends of research on rational and emotional advertising. The results of the analysis reveal the research weakness for this concept, especially in terms of definitions and conceptualization. Also, the results highlight the fragmented nature of the themes addressed in the various research articles on rational and emotional advertising.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.147 | 0.222 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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 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".