The Global Case Studies Textbook Project: Faculty-Student Research Collaboration in the Business French Classroom
Bibliographic record
Abstract
Despite the need for a new generation of entrepreneurs, higher education continues to train new cadres of employees while neglecting entrepreneurship as a viable career path. In this article, the senior author (a Business French instructor and an entrepreneur) and the junior author (an International Business undergraduate) describe an entrepreneurial class project that culminated in a published book of multimedia Business French (BF) case studies. The Business French class, French 423—divided into five research teams—researched, designed, and created five case studies each in English and French. The cases, published using Articulate Storyline, included a one-page case statement, YouTube videos depicting the firm or industry, comprehension activities, and a problem to solve. The cases focused on firms representing France, Canada, and Francophone Africa. San Diego State University’s national Language Acquisition Resource Center (LARC) and Montezuma Press will publish the case studies book with students receiving co-authorship in 2014.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".