Building a Regional Library-Led Case Competition: Reflections from Librarians and Vendor Partners
Bibliographic record
Abstract
Libraries are increasingly involved in supporting and facilitating case competitions, leading to competitions that emphasize decision-making and understanding of a larger ecosystem of information. This paper examines existing literature on the topics of librarian support for case competitions, the growing trend of library-led case competitions, and the expanded role of libraries in support of entrepreneurs, students, and faculty in entrepreneurial programs. The conversation is expanded through a discussion of the Midwest Entrepreneurship Case Competition (MECC), a library-led case competition that prioritized the participation of undergraduate students and grew from a local to a regional event. Vendor engagement, case development, competition format, timeline, and execution are shared and analyzed. Reflections from three stakeholders, including a first-time case competition planner, case competition judge, and vendor partner, enumerate MECC’s benefits to librarianship and student learning, approaches to information literacy, and opportunities for hands-on engagement in the development and marketing of tools. This research demonstrates the value of library-led case competitions as interventions for building transferable business information literacy skills and mechanisms for shaping and furthering collaboration among libraries, librarians, students, and vendors.
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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 | 0.062 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".