Confronting Student Distrust: Unexpected Findings from a Five-Week Business Intelligence Course
Bibliographic record
Abstract
In spring 2024, the Goizueta Business librarians at Emory University were invited to develop and teach a 10-hour, for-credit BBA Senior Seminar workshop. This experience gave the librarians an opportunity to scaffold content across a five-session series and to tackle the challenge of providing graduating undergraduate business students with the skills to conduct credible business research via Google when faced with limited access to databases on the job. Each session allowed the students to explore business intelligence frameworks around who owns information, as well as strategies for targeting credible sources and for sifting through the “noise” in Google’s returned results. The final session included a discussion about using generative AI versus Google for business research. Interspersed throughout each session were various exercises to test the students’ knowledge. During this process, the librarians learned many unexpected and difficult lessons about the best methods for engaging with undergraduate business students. In particular, the students were extremely reluctant to buy into business intelligence research methodologies and distrusted the librarians’ expertise as information professionals. The experience of teaching this class shook the business librarians’ confidence and unsettled most of their assumptions about the best methods for teaching business undergraduates.
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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 | 0.032 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".