Emotional Projective Mapping (EmoMap): A Pilot Study Examining a Tool for Collecting Emotional Responses to Food
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
ABSTRACT Emotions can provide meaningful information for understanding food choices. Most methods for collecting emotions in sensory evaluation are list‐based questionnaires. Lists, however, might reduce data accuracy and lead to lost information. Therefore, an adaptation of projective mapping for collecting emotional responses, called Emotional Projective Mapping (EmoMap) is being piloted. To validate the EmoMap, online, it was compared to two lexicon techniques (EsSense25/emotion circumplex). Pictures of eight foods were presented to 63 participants who answered how they expected to feel if they could eat the food. EmoMap was effective in discriminating and describing foods based on the emotional profile. Twenty‐seven emotion categories were created providing broad descriptive data for a holistic understanding of participants' perceptions. EmoMap can effectively be used to collect product‐specific emotional data in a single session. Data collected were used in an integrative analysis to show an overall relation between emotions, attributes, and liking.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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