Bibliographic record
Abstract
Objective - In developing marketing strategies for the Business & Economics Library (BEL) at the University of Illinois at Urbana–Champaign (UIUC), a survey was designed to answer the following questions: • Should BEL develop marketing strategies differently for East Asian business students? • What services do graduate business students want to receive from BEL? • With whom should BEL partner to increase visibility at the College of Business? Marketing research techniques were used to gather evidence upon which BEL could construct appropriate marketing strategies. Methods - A questionnaire was used with graduate business students enrolled at UIUC. The survey consisted of four categories of questions: 1) demographics, 2) assessment of current library services, 3) desired library services, and 4) research behavior. The data were analyzed using descriptive statistics and hypothesis testing to answer the three research questions. Results - East Asian business students showed similar assessment of current services as non-East Asian international business students. Survey results also showed that graduate business students had low awareness of current library services. The Business Career Services Office was identified as a co-branding partner for BEL to increase its visibility. Conclusion - A marketing research approach was used to help BEL make important strategic decisions before launching marketing campaigns to increase visibility to graduate business students at UIUC. As a result of the survey, a deeper understanding of graduate business students’ expectations and assessment of library services was gained. Students’ perceptions became a foundation that helped shape marketing strategies for BEL to increase its visibility at the College of Business. Creating marketing strategies without concrete data and analysis is a risky endeavor that librarians, not just corporate marketers, should avoid.
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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 | 0.061 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".