Bibliographic record
Abstract
Discussions about climate change are taking place more and more frequently, as awareness of its rapidly growing danger increases. Discussions about what we, as citizens, can do to counteract climate change’s effects are being shrouded by the loud voices that are denouncing the very existence of climate change. These dissenters are distracting us from valuable conversations about action and strategy, as focus is directed towards simply proving that actions must be taken in the first place. Knowing who these dissenters are is crucial in understanding how to get through to them about the existence of climate change and the importance of working slow its effects. Previous research has focused mainly on American citizens and their trust levels in various information sources about climate change information. When looking at the level of trust in news media, scientists, and the US President as sources of climate change information, we found that one’s level of education, political party affiliation, and voter history in the 2016 presidential election were predictors. With this knowledge, it is possible that we may tailor information sources that climate change deniers consume to aid in their understanding of the reality and impact of climate change. Faculty Mentor: Shelley Boulianne Department: Sociology
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.
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 | 0.007 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".