Bibliographic record
Abstract
Flip the calendar back to March 2020. Economic activity went to zero and the country experienced a shutdown. Unemployment rose and people stayed home and didn’t go about their normal routine of buying things. To try and rebound the economy, the Federal Reserve issued several rounds of stimulus checks. “The economic decisions that we saw in March 2020 were driven completely by the fact that we were shut down,” said Eric Higgins, research director and the von Waaden Chair of Investment Management in the College of Business Administration. “Once the country opened back up, those issues began to go away and the economy came back.” Fast forward two-plus years. Every day we hear about inflation, unemployment and the volatile market. The economy is experiencing an increased labor shortage because of retirements or decisions not to return to the workforce, Higgins said. But Higgins wanted to know: Is the economy really bad or are we still experiencing the aftereffects of March 2020? Higgins and several collaborators tried to find the answers by analyzing and comparing 2020 to the 2008 Great Recession. Their research shows that 2020 was not a repeat recession, but was the result of financial issues and decisions directly correlated to the pandemic. As businesses began to reopen and people left their houses, we began to see increased growth and demand for products. “If there is a shortage of labor and people want to purchase things, that means the price of labor is going to go up and the price of stuff is going to go up,” Higgins said. “The economy isn’t bad. I think the economy has rebounded, but it hasn’t normalized in terms of what the new normal looks like and that might take a while.”
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.311 | 0.188 |
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".