Le marketing du Neuromarketing : Enjeux académiques d’un domaine de recherche controversé
Bibliographic record
Abstract
Since the 1990s, a growing number of social science researchers collaborate in the creation of areas of research such as neurolaw, neuroeducation, neuroeconomy or neuromarketing. Sometimes referred to as ‘neurodisciplines’, these areas of research share a common postulate: the measure and analysis of the nervous system’s activity offers the possibility of discovering new ways of explaining human behaviour. Neuromarketing first appeared in the early 2000s and has developped in both university laboratories and private ones. Neuromarketers aim to understand consumer behaviour by applying neuroscientific theories and methods of measuring neurobiological activity to marketing questions. As a controversial topic, neuromarketing is critized in both the public space and academia. Some members of the media, some consumer associations and some neuromarketers see neuromarketing as having a more or less realistic power of persuasion (Lindstrom, 2009) while most neuroscientists qualify it as a scam or publicity stunt (Nature, 2004). Starting from bibliometric analysis of neuromarketing publications, we define the shifting boundaries of this area of research whose subject itself is still opened to debate. Building on Pierre Bourdieu’s work on the scientific field, we highlight the forces that shape this speciality both in and out of the academic field. Based on semidirective interviews, we demonstrate that neuromarketers have to develop discursive strategies to distance themselves from the controversial image of neuromarketing and adopt publication strategies in order to disseminate the results of their research in the scientific field.
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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 | 0.076 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.044 | 0.045 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.018 | 0.021 |
| 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".