From hospital to urban centre: transforming SUS Våland
Bibliographic record
Abstract
The objective of this thesis is to explore the transformation of the Våland hospital as the hospital functions are set to move to Ullandhaug. The research question guiding this study is: \n\nHow can the current hospital area be transformed into a new vibrant and multifunctional area covering the demands of the 21st century? \n\nThe study employs comprehensive research methodology, including literature review, analysis of the physical properties of the area, and the creation of a design proposal. These methods were chosen to gather relevant information, identify challenges, and propose feasible solutions for the development of the Våland hospital grounds.\n\nThrough the analysis, several challenges were identified, including barrier effects, the presence of large existing building masses, and a lack of identity. Understanding these challenges is important for addressing the needs and opportunities of the area effectively.\n\nThe proposed design framework focuses on redifining the purpose of existing buildings based on their functional and aesthetic potential. It also includes a well-connected road network design, measures for improved connectivity and traffic management, the integration of green spaces, and the establishment of a historical quarter to enhance the area’s identity\n\nThis master thesis serves as an inspirational starting point for future discussions, collaborations, and refinement of the development plans for the Våland hospital area. By addressing the research question and proposing a comprehensive design framework, this study contributes to the realization of a vibrant, connected, and identity-driven urban area that covers the demands of the 21st century.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".