Pain Points and Solutions: Bringing Data for Startups to Campus
Bibliographic record
Abstract
Entrepreneurship is growing as a cross- and inter-disciplinary area of focus for higher education. From patent and tech transfer offices to business, science, and engineering programs, the demand for entrepreneurship resources and support delivered via libraries is booming. Building library collections to help patrons design, launch, and run successful businesses is challenging: Market research and private equity/venture capital resources arrive at premium prices. Increasingly, these resources must interoperate with software used to clean, analyze, and visualize data. This data is often difficult to find and deploy. Restrictive, corporate-style licenses reflect that new vendors are not yet acclimated to the academic market’s access requirements and licensing constraints. This paper will share a framework for how to understand entrepreneurship in higher education and explain the types of information commonly requested by users. Such information often exists in disciplinary silos, emphasizing the importance of collaborative collection development across subject lines. The authors will explore the unique challenges to building collections that serve patrons developing new ventures. This includes collaborating with external stakeholders to fund resources that have not been traditionally purchased by libraries. Strategies for licensing data and other e-resources in this space will be discussed, including the central complications arising from universities as incubators for for-profit startups. The authors will suggest best practices for building relationships with stakeholders, developing relevant collections and services, and marketing these resources to support communities.
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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 | 0.018 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.036 |
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".