Accounting for the Uptick in Orange County Consumer Sentiment
Bibliographic record
Abstract
College Orange County Consumer Sentiment Index rose from a value of 94.8 in the third quarter of 2019 to a reading of 96.3 in the fourth quarter.The nearly two percent rise in consumer sentiment in Orange County likely reflects solid local and national economic conditions over the last three months.GDP growth for the third quarter of 2019 measured 2.1 percent and the Federal Reserve's three interest rate cuts have improved the local and national housing markets.Orange County has an unemployment rate of only 2.5 percent based on the November survey that has also been accompanied by high wage growth in the area.According to Marc Weidenmier, Professor of Finance at the Argyros School of Business and Economics and Director of the Orange County Consumer Sentiment Survey, "OC Consumer sentiment looks like a slow boat cruising on smooth waters."The small rise in consumer sentiment was driven by three questions in the seven question survey.The 500 random respondents in Orange County reported increased
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".