Global Viewpoints: The Effect of Geographic Background and Travel Experience on Choice of Study
Bibliographic record
Abstract
In any particular class, students rarely have the opportunity to select the topics that they study. So when given a choice, such as on a semester long research project, do students choose something close to home, or a topic that will require a more global perspective? This question is addressed using data from a survey of students in a range of social science and business courses. The analysis finds that if given the choice, students as a whole tend to focus on domestic topics in their research. Business students are more likely to conduct research on international topics than other students. Students that have spent more than a week outside of the United States and visited either Canada or Mexico are more likely to conduct research on an international topic, while students that have visited Asia are less likely to conduct research on an international topic. Additionally, survey results show that Farmingdale students have limited travel experience: 50 percent of the students surveyed have traveled no more than 4 times outside of the Northeastern United States; 66 percent of the students have spent at least one week outside of the U.S., and the top two destinations of these students are either in North America (Canada or Mexico) or the Caribbean Islands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".