How prevalent are suggestive brand names and Distinctive Assets? An AI-human approach
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
Despite the concept of a suggestive brand name existing for over one hundred years (Viehoever, 1920), the prevalence of suggestive versus non-suggestive brand names has not been documented. Previously, to do so extensively would have taken considerable time and money. We now show that artificial intelligence can replace manual coding with increased accuracy. We found the coding performances of Chat GPT-4 are 34% more accurate than GPT-3.5 and 44% more accurate than human coders. Systematically expanding our research to over 4,600 brands from consumer goods, services, and durables in major English-speaking markets (United Kingdom, United States, and Australia), we find that overall, slightly more than a quarter of all brand names are suggestive - ranging from 10% of durables to 56% of service brands. Further, we expand the suggestiveness research to non-brand name elements of almost 600 Distinctive Assets (e.g., colours, logos) across consumer goods, services, durables, and retailers (in the same three countries), finding that two in five are suggestive. The brand name and Distinctive Asset prevalence distributions are positively skewed, with most categories falling beneath the respective averages. Furthermore, regarding performance, on average, suggestive Distinctive Assets display lower levels of Fame and Uniqueness than non-suggestive Distinctive Assets.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| 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