Bibliographic record
Abstract
This report consists of my graduation plan, made during the first and second quarter of my graduation project. Argumentation of choice of the studio Fascination Since childhood I have been fascinated with renovation of existing buildings. If I remember correctly, especially English channels on television elaborated on that theme. People bought a barn, church, monastery or castle to make their dream come true, building their dream home in such historical places, which I found beautiful. I started my studies in architecture with the same dream in my mind. I hope some day I will be able to rebuilt my own dream house. I first came into contact with RMIT in the BSc6 studio project. Although every project has a context, it is present in the superlative when dealing with a transformation project. During my MSc1 I have done another RMIT project. What I really liked about this particular studio was the research part that it entails. To figure out the story behind a building or area was what I found very interesting. During my internship at Natuurmonumenten it was clear that restoration and reuse was really my “thing”. My choice for the RMIT graduation studio was not that difficult. Motivation studio During the time I had a bicycle in Delft, the route to the TU campus came past the Armamentarium. Twice a day I cycled past this striking building. When this building was an option for graduation it made me curious. What does the building look
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.301 | 0.235 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".