Bibliographic record
Abstract
Optimism about the U.S. economy's prospects improved sharply in the second quarter, but compensation freezes, layoffs and capital spending cuts were still on the horizon for about half of companies, according to a survey of CPA financial executives. Nearly one-fifth of respondents were optimistic or optimistic about the U.S. economy's outlook over the next 12 months, according to results from the Business & Industry Economic Outlook Survey Q2 2009, conducted by the AICPA and the University of North Carolina's Kenan-Flagler Business School. Barely 5% expressed similar optimism in the first quarter of 2009. It was the first time in almost two years that the proportion of respondents expressing optimism in the U.S. economy increased quarter to quarter. Recent government actions and the new administration, improving economic indicators and the availability of credit were cited as causes for optimism in open-ended comments by respondents. You have to keep in mind that we were probably at rock bottom in terms of CFOs' optimism a quarter ago, Tom Foard, CFO of Publishers Circulation Fulfillment Inc. in Towson, Md., told the JofA during the AICPA National CFO Conference in May. think you're seeing renewed optimism that we are going to come out of this and that the stimulus plan and the TARP program are going to work. [Regarding timing of recovery] I think we're looking at the middle of 2010. While more than half of respondents (53%) had a or outlook for the economy in the second quarter of 2009, overall pessimism was down from 83% in the first quarter. The percentage of respondents who were very pessimistic fell even more sharply--from nearly 21% in the first quarter to 7% in the second quarter. [Managers] have optimism for the economy as a whole, but I think they're cautiously optimistic. I would expect we'll see a quarter or two before firms begin to increase employment, increase spending, increase investment, said Mark Lang, professor of accounting at the University of North Carolina's Kenan-Flagler Business School. They will need to be a little bit more certain that this is actually a bottom before they actually proceed. The survey, conducted between April 22 and May 5, included responses from 995 CPAs. Fifty-nine percent were CFOs, 30% were controllers, and 4% were CEOs or COOs. Sixty-five percent of respondents work for privately owned entities, 14% for public companies, 12% for government, education and associations, and 6% for foreign-owned companies. …
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".