Bibliographic record
Abstract
Computing has become essential in virtually all physical fields, used for tasks such as modelling complex systems and analyzing data. As a result, computer programming competence is now considered a default requirement for physics research. Additionally, computer programming requires critical thinking and problem solving skills – both of which are also essential for physics and other rigorous disciplines. Thus, learning to program at the undergraduate level not only facilitates students’ ability to apply physical principles to solving problems, but also boosts marketable skills valuable in a more general job market. However, little emphasis is placed on computer literacy in the introductory courses of undergraduate physics curricula. Physics students interested in pursuing undergraduate research will often need to either take a computer science course or learn a computer programming language independently. In either case, it takes the student a long time to gain an understanding of the language and be able to apply it to relevant problems. This workshop is geared toward instructors and teaching assistants in introductory undergraduate physics courses with a working understanding of and experience using at least one programming language (e.g., Python, MATLAB, C++) for scientific applications. The intention is to introduce methods and provide suggestions for more effectively introducing students to scientific programming and integrating it into the physics curriculum.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.016 |
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".