Bibliographic record
Abstract
What makes a person liberal or conservative? Why does the Democratic Party scare off so many possible supporters? When does our "injustice trigger" get pulled, and how can fairness overcome our human need to look for a zero-sum outcome to our political battles?Tapping into a pop culture zeitgeist linking Bugs Bunny, Taylor Swift, and John Belushi; through popular science and the human brain; to our political predilections, arguments, and distrusts, Daniel Meegan suggests that fairness and equality are key elements missing in today's society. Having crossed the border to take up residency in Canada, Meegan, an American citizen, has seen first-hand how people enjoy as rights what Americans view as privileges. Fascinated with this tension, he suggests that American liberals are just missing the point. If progressives want to win the vote, they need to change strategy completely and champion government benefits for everyone, not just those of lower income. If everyone has access to inexpensive quality health care, open and extensive parental leave, and free postsecondary education, then everyone will be happier and society will be fair. The Left will also overcome an argument of the Right that successfully, though incongruously, appeals to the middle- and upper-middle classes: that policies that help the economically disadvantaged are inherently bad for others. Making society fair and equal, Meegan argues, would strengthen the moral and political position of the Democratic Party and place it in a position to revive American civic life. Fairness, he writes, should be selfishly enjoyed by everyone
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".