Pain Points and Solutions: Bringing Data for Startups to Campus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it