Bibliographic record
Abstract
“Grass would grow in the streets of a hundred cities,” President Herbert Hoover predicted, if his Democratic opponent were permitted to carry out his plans for large-scale federal intervention (Siracusa and Coleman 2002, 19). But by the time of the 1932 election, three years of economic contraction had already wrought devastation on an unprecedented scale. National income had been cut in half and 5,000 banks had collapsed, wiping out 9 million savings accounts. Most disconcerting was the persistence of plenty amidst want: while industry functioned at a small fraction of capacity, a substantial share of the population went without basic necessities. With fully one quarter of the workforce unemployed, many began to question whether the capitalist system itself would survive the damage. Against this backdrop of economic wreckage, Franklin Roosevelt defeated the deeply unpopular Republican incumbent with a stunning 57.4 percent of the vote while sweeping his party to new and vastly expanded majorities in the Senate and House of Representatives, respectively (Kennedy 1999). Having promised a “new deal” for the American people, Roosevelt moved fast to enact a flurry of novel policy responses to the deepening crisis. Within his first year in office, the president acted to shore up the financial system with an Emergency Banking Act, to relieve farmers with the Agricultural Adjustment Act, and to boost industrial prices and employment with the innovative National Industrial Recovery Act.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".