Awareness of artificial intelligence: Diffusion of information about AI versus ChatGPT in the United States
Bibliographic record
Abstract
This paper addresses the awareness about the artificial intelligence across states in the United States. We uniquely create indices of Google internet search results for general AI awareness and about ChatGPT, normalizing alternatively by internet users and land area. An understanding of the awareness about AI would provide useful insights into regulatory attempts to monitor and guard the AI technologies, besides suggesting alternatives for laggard states to catch up. Econometric results to explain the drivers of AI awareness show that, ceteris paribus, more prosperous states had greater awareness about AI and ChatGPT. On the other hand, states with greater economic freedom had a lower awareness. States with more men to women has lower AI awareness when hits were normalized by area, but the reverse was true when weighted by internet users. States with a higher proportion of the elderly population were no different from the other states, while those with greater urbanization had more AI/ChatGPT awareness when the internet hits were weighted by land area. Finally, states bordering Canada were no different from other states, while states bordering Mexico generally had a lower AI/ChatGPT awareness.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".