Only One in Six Mainers Expect Positive Impact From Artificial Intelligence 11/10/2025
Bibliographic record
Abstract
Very few Mainers expect artificial intelligence to have a positive impact on the country over the next decade, with young people particularly pessimistic. Medical care is the lone area where Maine residents are somewhat optimistic about the effects of AI but more than two-thirds predict negative consequences for news, relationships, and elections. While a majority of Mainers say they use AI at least occasionally, only a quarter report using it once a week or more often and only half say that their experience using it has been positive. Web searches and answering questions are the two most common uses for AI among Mainers. About half of working Mainers say that they use AI at work at least occasionally and one-third expect AI will harm their job security. Seven in ten expect that AI will lead to fewer jobs in over the next decade.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.218 | 0.093 |
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".