Most Latinos Say Democrats Care About Them and Work Hard for Their Vote, Far Fewer Say So of GOP
Bibliographic record
Abstract
ew Research Center conducted this study to understand the nuances of Hispanic political identity, Hispanics' views about some of the political issues being discussed in the U.S. today, and their interest in the upcoming 2022 midterm elections.For this analysis, we surveyed 7,647 U.S. adults, including 3,029 Hispanics, from Aug. 1-14, 2022. This includes 1,407 Hispanic adults on Pew Research Center's American Trends Panel (ATP) and 1,622 Hispanic adults on Ipsos' KnowledgePanel. Respondents on both panels are recruited through national, random sampling of residential addresses. Recruiting panelists by phone or mail ensures that nearly all U.S. adults have a chance of selection. This gives us confidence that any sample can represent the whole population, or in this case the whole U.S. Hispanic population. (See our "Methods 101" explainer on random sampling for more details.)To further ensure the survey reflects a balanced cross-section of the nation's Hispanic adults, the data is weighted to match the U.S. Hispanic adult population by age, gender, education, nativity, Hispanic origin group and other categories.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".