A Rose by Any Other Name: Would it Smell as Sweet?
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
We examined whether presenting an odor with a positive, neutral, or negative name would influence how people perceive it. In experiment 1, 40 participants rated 15 odors for their pleasantness, intensity, and arousal. In experiment 2, 30 participants passively smelled 10 odors while their skin conductance (SC), heart rate (HR), and sniffing were recorded. We found significant overall effects of odor names on perceived pleasantness, intensity, and arousal. Pleasantness showed the most robust effect of odor names: the same odors were perceived as more pleasant when presented with positive than with neutral and negative names and when presented with neutral than with negative names. In addition, odorants were rated as more intense when presented with negative than with neutral and positive names and as more arousing when presented with positive than with neutral names. Furthermore, SC and sniff volumes, but not HR, were modified by odor names, and the SC changes could not be accounted for by sniffing changes. Importantly, odor names presented with odorless water did not produce any effect on skin conductance and sniff volumes, ruling out the possibility that the naming-related findings were triggered by an emotional reaction to odor names. Taken together, these experiments show that there is a lot to a name, at least when it comes to olfactory perception.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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