Bibliographic record
Abstract
For this project, I researched different world views according to students who have studied abroad. The reason I chose this topic was because during my travels abroad, I met many people from other countries. Their views of where I am from were intriguing to me. For example, when I traveled as a child, when I said I was American, many people would stick up their noses. When I said I was from Chicago, I heard many phrases about what Chicago is famous for, the most frequent one being ???Oh Chicago! BANG! BANG!??? (in reference to Al Capone). During my recent trip to Egypt, I decided to experiment with this phenomenon. Because the Bush administration was looked down upon by other countries, many people told me when traveling I should say I was German or Canadian. My friends and I tried this for a few days, however we would get confused on which nationality we had decided to be each day. Eventually, we resorted back to claiming our American nationality, in hopes that we would not be killed because of our past president???s reputation. To our surprise, the reaction to our Egyptian friends was not that they hated out nation, but that they loved our current president and in consequence, loved Americans. Soon, the responses changed from their indifference to our ???German and Canadian nationalities??? to ???Obama! We love Obama! We love Americans!???.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.123 | 0.123 |
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".