Reading the space through ornamentation
Bibliographic record
Abstract
Canada real is a specific linear site outside of Madrid, occupied by low-income inhabitants. The site is fragmented into 5 sectors; however, in terms of building typology, atmosphere, and culture, it is much more diverse in each of the sections. With the filter of ornamentation as my individual topic, i investigated this strip. ornamentation has a rich historical background in architecture and the impulse of ornamentation not only leads to enhancing the formal and structural qualities, but also fills the need of visual and sensual pleasure, which helps the viewer to communicate with the space. The strip was quite divers in terms of typology, materialisation and ornamentation. as a viewer of the space one not only communicates with the space itself but also with its visual aspect. There are various vectors based on which space can be perceived differently. ornamentation is capable of creating uncommon feelings and impressions due to composing common elements. The combination of ordinary elements of a building such as walls and floor and blocks can be read as an ornament only because of an uncommon combination; ornament is neutral in its language but it is playful in its form. layered and in-between spaces, combination of different fragments, and the relation of different characteristics as the main ideas i took from the mapping led me to the idea of a bridge as an architectural space. This bridge is the combination of different characteristics in one, while itself is positioned as a pause between two spaces, uniting them. The program inside is not fixed and can change based on the needs. Because the variety of the characteristics in architectural aspect, the program also is diverse. In this bridge, everyone can have their own pathway, they can wander around narrow and small spaces inside and explore their own spatial interpretation of the bridge. ?
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".