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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".