The Impact of Intellectual Ideologies in Shaping the Urban Townscape from the Middle Ages to the Contemporary: The City of Baghdad Between Yesterday and Today
Bibliographic record
Abstract
Intellectual ideological transformations affect the formation of the townscape, which in turn affects the narratives of the townscape constantly, as cities undergo various transformations, including natural and sudden forced ones, through economic and social processes; accompanied by structural changes in its urban scenes to a different formal model, reflecting the spirit of the age. The townscape is the reflective text that highlights the city in the form of a serial vision and scenes that carry the effects of the past and simulate the era, where the townscape is basically built through successive visions. The research deals with the difference in the concept of intellectual ideals from the Renaissance era based on perspective and artistic concepts to technology-based aesthetics in the international style, as this led to a change in the integrated coherent scene (the traditional scene) to the technology-based scene (the technological scene), and the latest discontinuity in the narrative of the story line. The landscape in urban city centers has both intellectual and physical levels. The discontinuity occurs in the townscape at the intellectual and material levels, when the concept of aesthetics and ideals varies in different periods of time. The research aims to study the impact of the entry of different intellectual trends (ideals of each era) on the townscape, in Al-Rasheed Street in Baghdad, during different periods of time. And reading the state of interruption in the scene and its impact on the perception of the recipient.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".