Bibliographic record
Abstract
In 1979 President Carter surprised the American public by telling them that they suffered from malaise. Most people shrugged or laughed. The next year the voters decided the problem was not with them but with the government, especially Jimmy Carter's government. Now malaise is back in a new and more contagious form. This time voters have been telling their governments that they are unhappy, and the governments have been trying to reassure them that their gloom is unwarranted. This time malaise is not limited to the United States. The signs are widespread. In Canada, France, Germany, and Italy votes have increased sharply for splinter parties, dissidents, and in some cases extremists. In the United States, more than 80% of the public tell pollsters that the country is "on the wrong track." Perhaps reflecting the mood of the voters, large numbers of U.S. Congressmen have given up their seats. As they retire, some use the opportunity to comment on the failures of the political system, and its inability to resolve problems. Voters interviewed after the spring primary elections expressed more than the usual dissatisfaction with all of the candidates.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".