Campus entrepreneurs’ research habits and needs: a five-year study
Bibliographic record
Abstract
Purpose This study surveyed the entrepreneurship community on a large university campus in 2016 and in 2020 to identify and understand its information habits and needs. User needs can inform service design and assessment, as well as inform approaches to reference interactions. Librarians are encouraged to conduct similar surveys to better understand this niche population. Design/methodology/approach Investigators employed repeated cross-sectional design, a longitudinal research approach that draws on samples of non-overlapping or minimally overlapping cases over time. Qualitative and quantitative data were collected using online survey instruments. Data collected included demographic information, venture characteristics, participation in institutional activities such as accelerator programs and credit courses, general startup research behaviors and needs, and details of a specific instance of business or market research as well as interaction with the library and access to training. Triangulation of semantic and episodic was applied to draw reliable conclusions about respondent behavior. Findings In both surveys, over half of respondents were students and 75% of respondents were engaged in startup activity, most at the early stages. While respondent demographics, type and purpose of information sought remained constant between the two surveys, awareness and use of the library rose on several metrics. Coding revealed insights into respondents’ attitudes toward and strategies for secondary business research. Information obtained during the research process had a moderate impact on their ventures. These findings informed the development of library research and instruction services, programs, and collections for entrepreneurs. Originality/value The repeated cross-sectional design of the study is unique and shows trends in the community over time. The mixed-methods approach provides a robust and nuanced portrait of the community. These findings informed the development and assessment of library research and instruction services, programs and collections for entrepreneurs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".