Frequent pro-climate messaging does not predict pro-climate voting by United States legislators
Bibliographic record
Abstract
Abstract Legislators who frequently advocate for climate action might be expected to cast more pro-climate votes, but pro-climate messaging alone may not predict actual voting behavior. We analyzed 401 539 tweets posted by 518 United States federal legislators over the 6 months prior to the 2020 election and identified 5350 of these as containing climate-relevant messaging. Of the 4881 tweets that we coded as promoting climate awareness or supporting action (‘pro-climate’), 92% were posted by Democratic legislators while all 138 tweets undermining climate awareness or opposing action (‘anti-climate’) were posted by Republicans. Constituent support for Congressional climate action was only weakly related to the rate of pro-climate tweeting by legislators. Overall, we found that increased pro-climate tweeting was not a significant predictor of pro-climate voting when controlling for party affiliation and constituent support for climate action. We conclude that climate-concerned voters would be best served by using party affiliation rather than climate-related messaging to judge the pro-climate voting intentions of United States legislators.
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How this classification was reachedexpand
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".