Bibliographic record
Abstract
Abstract – This article is on a university course called Engineering Conferences. It has been developed by the authors and installed as a mandatory part of the curriculum in three master’s degree programs for engineering students. The participants of the course are postgraduates with different nationalities, mostly German, and different technical backgrounds. They study Mechanical Engineering, Simulation and Experimental Technology, or International Business Engineering. The basic idea of the course concept goes far beyond simply teaching the standards of academic writing and skills for using scientific publications. By using a learnercentered approach, the students get engaged in typical activities around an active attendance of a real conference. Students learn to locate the field of their bachelor thesis or project report in the world of research communities, scientific journals and engineering conferences. They learn about conferences matching their bachelor/project topic. They write a paper complying with common academic standards, submit it to a mock-up conference, and review submissions of their fellow students. Students also produce a poster and have to defend it in a poster session held publicly on the campus. Engineering Conferences is a course on scientific communication and presentation that also aims at the development of other skills and competences needed in the world of modern engineering.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.438 | 0.230 |
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".