Bibliographic record
Abstract
Abstract Motivation research is a term used to refer to a selection of qualitative research methods that were designed to probe consumers' minds in order to discover the subconscious or latent reasons and goals underlying everyday consumption and purchasing behaviors. Motivation research was derived from an application of Sigmund Freud's psychoanalytic personality theories and became the premier consumer research method in the 1950s. It has had a lasting influence on the areas of advertising and consumer research, as well as advertising practice. Dr. Ernest Dichter, a trained psychoanalyst, was the first and foremost practitioner of motivation research. Critics of motivation research disliked its small sample sizes, its subjective interpretations, its basis in clinical methods, and its exotic explanations. Despite its controversial past, motivation research is still regarded as an important technique by marketers who want to gain a deeper understanding into consumer motivations. Current techniques in wide use derive directly from motivation research. These methods include projective tests, metaphor analysis, storytelling, word‐association tasks, sentence‐completion tasks, picture generation, and also photo sorts. Some of the most prominent contemporary marketing researchers such as Gerald Zaltman and Clotaire Rapaille use techniques that are recognizably affiliated with motivation 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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.099 | 0.030 |
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".