Le marketing du Neuromarketing : Enjeux académiques d’un domaine de recherche controversé
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
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.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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