Bibliographic record
Abstract
On ‘Target’ for success Jules Kotrba was exposed to the world as a student in the University of North Dakota's College of Business and Public Administration. And that's made all the difference in her career today as a senior business partner for Target Corp., only six years after graduating. Kotrba left UND in 2007 with a bachelor's degree in marketing and minors in international business and Chinese business and culture. She chose her minors because they gave her a business view on an international scale. That strategy is paying off big for her. Today, Kotrba is stationed at Target's headquarters in Minneapolis, where she is putting her international business experience – gained at UND – to good use. She's currently involved in the expansion of Target Stores into Canada. As a senior business partner she works with a team to manage relationships with Canadian business partners along with helping products move through the supply chain. She even became a U.S. Licensed Customs Broker to make sure Target products can be delivered across the border. "It feels like I'm working on a startup business," Kotrba said. "I'm doing something that hasn't been done before but with Target's backing." In college, Kotrba participated in a China study abroad program and loved it. It was in China where she finally made the connection with international business. She enjoyed learning how the market in China compared to the United States. Consumer behavior classes really interested her as well. She was fascinated on how consumers made decisions. Business and economics statistics and macroeconomics helped her analyze trends in sales and also taught her how the economy works through supply and demand, trade and economies of scale. Kotrba recently returned to UND to take part in the 26th annual Hultberg Lectureship, an annual event put on by the College of Business that features a panel of prominent and successful alumnae. This year's lectures also included UND Community Relations Officer Amanda Bentow and Shawn Deisz, manager of accounting for Basin Electric Power Coop. Kotrba started at Target Corporation as a business analyst for Women's and Seasonal Shoes and has now been working on the Global Trade team for three years. Prior to that, she taught English at the University of Shanghai for Science and Technology, which has had a strong relationship with UND for many years. Kotrba remains active in the Twin Cities, becoming fluent in Mandarin Chinese, as well as playing volleyball, softball and golf. She also is involved with Best Prep's Minnesota Business Venture and the Minnesota International Center. Kotrba keeps in touch with her alma mater by sitting on UND's College of Business Advisory Counsel and visiting with former colleagues at the UND Center for Innovation, where she once worked. She also meets with members of her former sorority, Alpha Chi Omega, and serves as a mentor for other students. Kotrba tells students to keep setting goals. At the age of 14, her mother signed her up for goal-making workshops and she is very thankful for that. In fact, she recently hired a life coach to keep her on track with achieving her goals. Her advice for up-and-coming business women is "it is important to be confident, connected, professional and well spoken. Listening skills are number one. Make sure your jobs line up to your dream job." Humbly, Kotrba contributes her success to her mother, also a businesswoman. "She let me know anything is possible." Kate Menzies University Relations student writer
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.144 | 0.056 |
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".