Professor David Flynn is jumping at the chance to study this booming state
Bibliographic record
Abstract
Professor David Flynn is jumping at the chance to study this booming state North Dakota: It's more than just oil. That's why folks like David Flynn, a University of North Dakota professor of economics, are jumping at the chance to study this booming state. As the head of the Bureau of Business and Economic Research, Flynn is researching the oil boom and other factors that could have long-lasting effects on the North Dakota economy, population and housing. "Economists have gotten the reputation of being the bearers of bad news," said Flynn. He knows that the black gold won't make the economy stay booming forever. Like all booms, Flynn predicts this too will eventually slow as a result of growing too much too fast. Most media attention is directed toward the oil boom; however, Flynn recognizes there are many subtle factors at play. The state's agricultural growth and the expansion of the retail sector from Canadian traffic helped spur the economy. North Dakota also stayed above the red because it was one of the few states in which banks did not participate in subprime mortgage loans. These loans were deemed a major factor in the 2008 recession. There is no question that the oil boom has caused people to flock to the state in search of jobs. The question on economists' minds is whether or not this population growth will be sustainable. That is precisely why Flynn has turned his attention to studying local businesses. He is curious to know if bringing additional services and businesses to towns near the oil fields will give these temporary residents a reason to stay. In another study, Flynn and a colleague from the University of Nebraska-Omaha picked South Dakota as a central meeting point to observe the survival of rural businesses there. Through their research, they found that rural businesses in South Dakota seem to survive more than urban businesses in particular sectors for reasons they are still investigating. When not knee-deep in research, Flynn discusses the changes in North Dakota's economy with his students. "The growth has made for an interesting case study for population and forecasting topics as a positive spot in the U.S. economy," said Flynn. Flynn teaches courses in economic forecasting, statistics, banking and bank regulations, and population analysis. He enjoys being able to integrate lectures with topics he is currently studying. Because of Flynn's research, UND is getting an up-close look at the state's changes and students are getting hands-on experience in the field of economics. How long will North Dakota keep booming? That's hard to say. Rest assured economists like Flynn have their nose to the grindstone, eager to find out. Kate Menzies University & Public Affairs student writer
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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 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".