A Trump presidency and the prospect for equality and diversity
Bibliographic record
Abstract
[Extract] During the 2016 US Presidential election, Republican Candidate (now President) Donald J. Trump made headlines around the world for vitriolic and inflammatory remarks against women, immigrants, Muslims, and other minority groups. Trump promised to build a wall along the Mexican border, ban Muslims from entering the USA, tighten abortion laws, repeal the Affordable Care Act ("Obamacare"), and restrict press freedom (Bulman, 2016). His vision to "Make America Great Again" appealed to many white, working class Americans who were angry at or resentful of immigrants, a lack of opportunities for themselves, ideological-based terrorist attacks, and political correctness (Boag, 2016). Many lesser-educated white voters also felt abandoned by progressives (Vance, 2016). Indeed, a recent study has shown that sexism and racism predicted support for Trump much more than economic dissatisfaction (Schnaffer et al., 2017).
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.020 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".