Webbbaserat magisterprogram i energiteknik. Slutrapport
Bibliographic record
Abstract
A Master/Magister program in sustainable energy engineering was planned and developed as part of the project “Webbbaserat magisterprogram i energiteknik”, sponsored by NSHU. The program had substantial discussions during the preparation phase and substantial progress was made towards a common strategy from the six partner universities (Umeå, Gävle, Linköping, Uppsala, Karlstad, KTH) who decided to participate in the endeavour. The program was first launched in 2007 with 18 initial students. It was re-launched in 2008 with 15 students and is now in the application phase for the fall 2009 intake. \n\nThe present report covers the final part of the project from the point of view of NSHU, but the educational efforts, and the cooperation between most of the partners, will continue into the fall 2009 intake at least, with own funding from the partner universities. The continuation of the project for 2010 onwards will be re-evaluated in the autumn 2009 based on decisions by the Swedish government regarding eventual tuition fees for non-European students.\n\nThis final report covers the background to the application, the development phase and gives some conclusions of what went well and where the major challenges were.\n\nThe project has been used as example for a national cooperation in the energy education at a few workshops and keynote speeches at conferences over the last two years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.418 | 0.337 |
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".