Bibliographic record
Abstract
This capstone paper introduces a special section of the already existing, and quite successful, University 101 programs at the University of South Carolina (USC). U101: Global Perspectives is a semester-long program proposal for first-year students (freshman and transfer students), offered by the Study Abroad Office (SAO) at USC. Functioning as part of the Global Carolina initiative, the USC SAO is committed to providing students with the knowledge and cultural awareness to be responsible and successful citizens in today’s globally integrated world. With the goal of increasing the number of students studying abroad, as well as creating unique opportunities for students during their first year on campus, the SAO has created a new course. The beginning of the course will focus on various cultural identifiers (i.e. religion, literature, film, language, politics, history, and food) in the larger context. The curriculum will lead students through a deeper way of thinking about culture and will become more specific throughout the semester. During Fall Break, students will travel to Quebec City, Canada to conduct research on the cultural identifier that they have chosen. Upon returning to campus, individual research projects will be presented on campus. U101: Global Perspectives seeks to contribute to the field of programming for first-year students, with this capstone paper serving as the initial steps towards adoption by USC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.230 | 0.097 |
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".