MétaCan
Menu
Back to cohort

Neuromarketing

2015· other· en· W4243921000 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWiley Encyclopedia of Management · 2015
Typeother
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsConcordia University
Fundersnot available
KeywordsNeuromarketingElectroencephalographyFunctional magnetic resonance imagingTask (project management)Cognitive sciencePsychologyNeuroscienceBrain activity and meditationPost hocComputer scienceCognitive psychologyMedicineEngineering

Abstract

fetched live from OpenAlex

Abstract Neuromarketing utilizes apparatuses from the neurosciences (e.g., functional magnetic resonance imaging or electroencephalography) to investigate individuals' response to marketing stimuli. The assumption is that such data will garner insights beyond those obtained via more traditional methods. Brain activation patterns are contrasted across two states: when performing an experimental task (e.g., viewing an advertisement) versus in a control condition. By mapping the changes in the chosen substrate (electric/magnetic signals, blood flow, or blood oxygenation), researchers infer the brain regions that were differentially engaged during the task. Some critics have argued that this paradigm amounts to little more than a search for “pretty brain images” followed by post hoc explanations while supporters propose that the paradigm will unlock many mysteries of the consumer's mind.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.022
GPT teacher head0.315
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it